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Dockerfile

@@ -17,8 +17,7 @@ WORKDIR /workspace
 
 RUN git clone https://github.com/SWivid/F5-TTS.git \
     && cd F5-TTS \
-    && pip install --no-cache-dir -r requirements.txt \
-    && pip install --no-cache-dir -r requirements_eval.txt
+    && pip install -e .[eval]
 
 ENV SHELL=/bin/bash
 

README_REPO.md

@@ -16,230 +16,112 @@
 
 ### Thanks to all the contributors !
 
-## Installation
-
-Clone the repository:
-
-```bash
-git clone https://github.com/SWivid/F5-TTS.git
-cd F5-TTS
-```
-
-Install torch with your CUDA version, e.g. :
-
-```bash
-pip install torch==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
-pip install torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
-```
+## News
+- **2024/10/08**: F5-TTS & E2 TTS base models on [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS), [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN).
 
-Install other packages:
+## Installation
 
 ```bash
-pip install -r requirements.txt
-```
+# Create a python 3.10 conda env (you could also use virtualenv)
+conda create -n f5-tts python=3.10
+conda activate f5-tts
 
-**[Optional]**: We provide [Dockerfile](https://github.com/SWivid/F5-TTS/blob/main/Dockerfile) and you can use the following command to build it.
-```bash
-docker build -t f5tts:v1 .
+# Install pytorch with your CUDA version, e.g.
+pip install torch==2.3.0+cu118 torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
 ```
 
-### Development
+Then you can choose from a few options below:
 
-When making a pull request, please use pre-commit to ensure code quality:
+### 1. As a pip package (if just for inference)
 
 ```bash
-pip install pre-commit
-pre-commit install
+pip install git+https://github.com/SWivid/F5-TTS.git
 ```
 
-This will run linters and formatters automatically before each commit.
-
-Manually run using: 
+### 2. Local editable (if also do training, finetuning)
 
 ```bash
-pre-commit run --all-files
-```
-
-Note: Some model components have linting exceptions for E722 to accommodate tensor notation
-
-
-## Prepare Dataset
-
-Example data processing scripts for Emilia and Wenetspeech4TTS, and you may tailor your own one along with a Dataset class in `model/dataset.py`.
-
-```bash
-# prepare custom dataset up to your need
-# download corresponding dataset first, and fill in the path in scripts
-
-# Prepare the Emilia dataset
-python scripts/prepare_emilia.py
-
-# Prepare the Wenetspeech4TTS dataset
-python scripts/prepare_wenetspeech4tts.py
+git clone https://github.com/SWivid/F5-TTS.git
+cd F5-TTS
+pip install -e .
 ```
 
-## Training & Finetuning
-
-Once your datasets are prepared, you can start the training process.
-
+### 3. Build from dockerfile
 ```bash
-# setup accelerate config, e.g. use multi-gpu ddp, fp16
-# will be to: ~/.cache/huggingface/accelerate/default_config.yaml     
-accelerate config
-accelerate launch train.py
-```
-An initial guidance on Finetuning [#57](https://github.com/SWivid/F5-TTS/discussions/57).
-
-Gradio UI finetuning with `finetune_gradio.py` see [#143](https://github.com/SWivid/F5-TTS/discussions/143).
-
-### Wandb Logging
-
-By default, the training script does NOT use logging (assuming you didn't manually log in using `wandb login`).
-
-To turn on wandb logging, you can either:
-
-1. Manually login with `wandb login`: Learn more [here](https://docs.wandb.ai/ref/cli/wandb-login)
-2. Automatically login programmatically by setting an environment variable: Get an API KEY at https://wandb.ai/site/ and set the environment variable as follows:
-
-On Mac & Linux:
-
-```
-export WANDB_API_KEY=<YOUR WANDB API KEY>
-```
-
-On Windows:
-
-```
-set WANDB_API_KEY=<YOUR WANDB API KEY>
+docker build -t f5tts:v1 .
 ```
-Moreover, if you couldn't access Wandb and want to log metrics offline, you can the environment variable as follows:
 
-```
-export WANDB_MODE=offline
-```
 
 ## Inference
 
-The pretrained model checkpoints can be reached at [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS) and [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), or automatically downloaded with `inference-cli` and `gradio_app`.
+### 1. Gradio App
 
-Currently support 30s for a single generation, which is the **TOTAL** length of prompt audio and the generated. Batch inference with chunks is supported by `inference-cli` and `gradio_app`. 
-- To avoid possible inference failures, make sure you have seen through the following instructions.
-- A longer prompt audio allows shorter generated output. The part longer than 30s cannot be generated properly. Consider using a prompt audio <15s.
-- Uppercased letters will be uttered letter by letter, so use lowercased letters for normal words. 
-- Add some spaces (blank: " ") or punctuations (e.g. "," ".") to explicitly introduce some pauses. If first few words skipped in code-switched generation (cuz different speed with different languages), this might help.
-
-### CLI Inference
-
-Either you can specify everything in `inference-cli.toml` or override with flags. Leave `--ref_text ""` will have ASR model transcribe the reference audio automatically (use extra GPU memory). If encounter network error, consider use local ckpt, just set `ckpt_file` in `inference-cli.py`
-
-for change model use `--ckpt_file` to specify the model you want to load,  
-for change vocab.txt use `--vocab_file` to provide your vocab.txt file.
-
-```bash
-python inference-cli.py \
---model "F5-TTS" \
---ref_audio "tests/ref_audio/test_en_1_ref_short.wav" \
---ref_text "Some call me nature, others call me mother nature." \
---gen_text "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences."
-
-python inference-cli.py \
---model "E2-TTS" \
---ref_audio "tests/ref_audio/test_zh_1_ref_short.wav" \
---ref_text "对，这就是我，万人敬仰的太乙真人。" \
---gen_text "突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道，我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？"
-
-# Multi voice
-python inference-cli.py -c samples/story.toml
-```
-
-### Gradio App
 Currently supported features:
-- Chunk inference
-- Podcast Generation
-- Multiple Speech-Type Generation
 
-You can launch a Gradio app (web interface) to launch a GUI for inference (will load ckpt from Huggingface, you may also use local file in `gradio_app.py`). Currently load ASR model, F5-TTS and E2 TTS all in once, thus use more GPU memory than `inference-cli`.
+- Basic TTS with Chunk Inference
+- Multi-Style / Multi-Speaker Generation
+- Voice Chat powered by Qwen2.5-3B-Instruct
 
 ```bash
-python gradio_app.py
-```
+# Launch a Gradio app (web interface)
+f5-tts_infer-gradio
 
-You can specify the port/host:
+# Specify the port/host
+f5-tts_infer-gradio --port 7860 --host 0.0.0.0
 
-```bash
-python gradio_app.py --port 7860 --host 0.0.0.0
+# Launch a share link
+f5-tts_infer-gradio --share
 ```
 
-Or launch a share link:
+### 2. CLI Inference
 
 ```bash
-python gradio_app.py --share
-```
-
-### Speech Editing
+# Run with flags
+# Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
+f5-tts_infer-cli \
+--model "F5-TTS" \
+--ref_audio "ref_audio.wav" \
+--ref_text "The content, subtitle or transcription of reference audio." \
+--gen_text "Some text you want TTS model generate for you."
 
-To test speech editing capabilities, use the following command.
+# Run with default setting. src/f5_tts/infer/examples/basic/basic.toml
+f5-tts_infer-cli
+# Or with your own .toml file
+f5-tts_infer-cli -c custom.toml
 
-```bash
-python speech_edit.py
+# Multi voice. See src/f5_tts/infer/README.md
+f5-tts_infer-cli -c src/f5_tts/infer/examples/multi/story.toml
 ```
 
-## Evaluation
+### 3. More instructions
 
-### Prepare Test Datasets
+- In order to have better generation results, take a moment to read [detailed guidance](src/f5_tts/infer).
+- The [Issues](https://github.com/SWivid/F5-TTS/issues?q=is%3Aissue) are very useful, please try to find the solution by properly searching the keywords of problem encountered. If no answer found, then feel free to open an issue.
 
-1. Seed-TTS test set: Download from [seed-tts-eval](https://github.com/BytedanceSpeech/seed-tts-eval).
-2. LibriSpeech test-clean: Download from [OpenSLR](http://www.openslr.org/12/).
-3. Unzip the downloaded datasets and place them in the data/ directory.
-4. Update the path for the test-clean data in `scripts/eval_infer_batch.py`
-5. Our filtered LibriSpeech-PC 4-10s subset is already under data/ in this repo
 
-### Batch Inference for Test Set
+## [Training](src/f5_tts/train)
 
-To run batch inference for evaluations, execute the following commands:
 
-```bash
-# batch inference for evaluations
-accelerate config  # if not set before
-bash scripts/eval_infer_batch.sh
-```
-
-### Download Evaluation Model Checkpoints
+## [Evaluation](src/f5_tts/eval)
 
-1. Chinese ASR Model: [Paraformer-zh](https://huggingface.co/funasr/paraformer-zh)
-2. English ASR Model: [Faster-Whisper](https://huggingface.co/Systran/faster-whisper-large-v3)
-3. WavLM Model: Download from [Google Drive](https://drive.google.com/file/d/1-aE1NfzpRCLxA4GUxX9ITI3F9LlbtEGP/view).
 
-### Objective Evaluation
+## Development
 
-Install packages for evaluation:
+Use pre-commit to ensure code quality (will run linters and formatters automatically)
 
 ```bash
-pip install -r requirements_eval.txt
-```
-
-**Some Notes**
-
-For faster-whisper with CUDA 11:
-
-```bash
-pip install --force-reinstall ctranslate2==3.24.0
+pip install pre-commit
+pre-commit install
 ```
 
-(Recommended) To avoid possible ASR failures, such as abnormal repetitions in output:
+When making a pull request, before each commit, run: 
 
 ```bash
-pip install faster-whisper==0.10.1
+pre-commit run --all-files
 ```
 
-Update the path with your batch-inferenced results, and carry out WER / SIM evaluations:
-```bash
-# Evaluation for Seed-TTS test set
-python scripts/eval_seedtts_testset.py
+Note: Some model components have linting exceptions for E722 to accommodate tensor notation
 
-# Evaluation for LibriSpeech-PC test-clean (cross-sentence)
-python scripts/eval_librispeech_test_clean.py
-```
 
 ## Acknowledgements
 

app.py

@@ -11,6 +11,7 @@ import soundfile as sf
 import torchaudio
 from cached_path import cached_path
 from pydub import AudioSegment
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 try:
     import spaces
@@ -27,16 +28,14 @@ def gpu_decorator(func):
         return func
 
 
-from model import DiT, UNetT
-from model.utils import (
-    save_spectrogram,
-)
-from model.utils_infer import (
+from f5_tts.model import DiT, UNetT
+from f5_tts.infer.utils_infer import (
     load_vocoder,
     load_model,
     preprocess_ref_audio_text,
     infer_process,
     remove_silence_for_generated_wav,
+    save_spectrogram,
 )
 
 vocos = load_vocoder()
@@ -53,6 +52,31 @@ E2TTS_ema_model = load_model(
     UNetT, E2TTS_model_cfg, str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors"))
 )
 
+chat_model_state = None
+chat_tokenizer_state = None
+
+
+def generate_response(messages, model, tokenizer):
+    """Generate response using Qwen"""
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True,
+    )
+
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=512,
+        temperature=0.7,
+        top_p=0.95,
+    )
+
+    generated_ids = [
+        output_ids[len(input_ids) :] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+    return tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
 
 @gpu_decorator
 def infer(ref_audio_orig, ref_text, gen_text, model, remove_silence, cross_fade_duration=0.15, speed=1):
@@ -147,8 +171,8 @@ with gr.Blocks() as app_credits:
 # Credits
 
 * [mrfakename](https://github.com/fakerybakery) for the original [online demo](https://huggingface.co/spaces/mrfakename/E2-F5-TTS)
-* [RootingInLoad](https://github.com/RootingInLoad) for the podcast generation
-* [jpgallegoar](https://github.com/jpgallegoar) for multiple speech-type generation
+* [RootingInLoad](https://github.com/RootingInLoad) for initial chunk generation and podcast app exploration
+* [jpgallegoar](https://github.com/jpgallegoar) for multiple speech-type generation & voice chat
 """)
 with gr.Blocks() as app_tts:
     gr.Markdown("# Batched TTS")
@@ -250,7 +274,7 @@ with gr.Blocks() as app_podcast:
 
 
 def parse_speechtypes_text(gen_text):
-    # Pattern to find (Emotion)
+    # Pattern to find {speechtype}
     pattern = r"\{(.*?)\}"
 
     # Split the text by the pattern
@@ -324,7 +348,6 @@ with gr.Blocks() as app_emotional:
     # Keep track of current number of speech types
     speech_type_count = gr.State(value=0)
 
-    # Function to add a speech type
     # Function to add a speech type
     def add_speech_type_fn(speech_type_count):
         if speech_type_count < max_speech_types - 1:
@@ -350,6 +373,7 @@ with gr.Blocks() as app_emotional:
         def delete_speech_type_fn(speech_type_count):
             # Prepare updates
             row_updates = []
+
             for i in range(max_speech_types - 1):
                 if i == index:
                     row_updates.append(gr.update(visible=False))
@@ -492,6 +516,166 @@ with gr.Blocks() as app_emotional:
         outputs=generate_emotional_btn,
     )
 
+
+with gr.Blocks() as app_chat:
+    gr.Markdown(
+        """
+# Voice Chat
+Have a conversation with an AI using your reference voice! 
+1. Upload a reference audio clip and optionally its transcript.
+2. Load the chat model.
+3. Record your message through your microphone.
+4. The AI will respond using the reference voice.
+"""
+    )
+
+    load_chat_model_btn = gr.Button("Load Chat Model", variant="primary")
+
+    chat_interface_container = gr.Column(visible=False)
+
+    def load_chat_model():
+        global chat_model_state, chat_tokenizer_state
+        if chat_model_state is None:
+            show_info = gr.Info
+            show_info("Loading chat model...")
+            model_name = "Qwen/Qwen2.5-3B-Instruct"
+            chat_model_state = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+            chat_tokenizer_state = AutoTokenizer.from_pretrained(model_name)
+            show_info("Chat model loaded.")
+
+        return gr.update(visible=False), gr.update(visible=True)
+
+    load_chat_model_btn.click(load_chat_model, outputs=[load_chat_model_btn, chat_interface_container])
+
+    with chat_interface_container:
+        with gr.Row():
+            with gr.Column():
+                ref_audio_chat = gr.Audio(label="Reference Audio", type="filepath")
+            with gr.Column():
+                with gr.Accordion("Advanced Settings", open=False):
+                    model_choice_chat = gr.Radio(
+                        choices=["F5-TTS", "E2-TTS"],
+                        label="TTS Model",
+                        value="F5-TTS",
+                    )
+                    remove_silence_chat = gr.Checkbox(
+                        label="Remove Silences",
+                        value=True,
+                    )
+                    ref_text_chat = gr.Textbox(
+                        label="Reference Text",
+                        info="Optional: Leave blank to auto-transcribe",
+                        lines=2,
+                    )
+                    system_prompt_chat = gr.Textbox(
+                        label="System Prompt",
+                        value="You are not an AI assistant, you are whoever the user says you are. You must stay in character. Keep your responses concise since they will be spoken out loud.",
+                        lines=2,
+                    )
+
+        chatbot_interface = gr.Chatbot(label="Conversation")
+
+        with gr.Row():
+            with gr.Column():
+                audio_output_chat = gr.Audio(autoplay=True)
+            with gr.Column():
+                audio_input_chat = gr.Microphone(
+                    label="Speak your message",
+                    type="filepath",
+                )
+
+        clear_btn_chat = gr.Button("Clear Conversation")
+
+        conversation_state = gr.State(
+            value=[
+                {
+                    "role": "system",
+                    "content": "You are not an AI assistant, you are whoever the user says you are. You must stay in character. Keep your responses concise since they will be spoken out loud.",
+                }
+            ]
+        )
+
+        # Modify process_audio_input to use model and tokenizer from state
+        def process_audio_input(audio_path, history, conv_state):
+            """Handle audio input from user"""
+            if not audio_path:
+                return history, conv_state, ""
+
+            text = ""
+            text = preprocess_ref_audio_text(audio_path, text)[1]
+
+            if not text.strip():
+                return history, conv_state, ""
+
+            conv_state.append({"role": "user", "content": text})
+            history.append((text, None))
+
+            response = generate_response(conv_state, chat_model_state, chat_tokenizer_state)
+
+            conv_state.append({"role": "assistant", "content": response})
+            history[-1] = (text, response)
+
+            return history, conv_state, ""
+
+        def generate_audio_response(history, ref_audio, ref_text, model, remove_silence):
+            """Generate TTS audio for AI response"""
+            if not history or not ref_audio:
+                return None
+
+            last_user_message, last_ai_response = history[-1]
+            if not last_ai_response:
+                return None
+
+            audio_result, _ = infer(
+                ref_audio,
+                ref_text,
+                last_ai_response,
+                model,
+                remove_silence,
+                cross_fade_duration=0.15,
+                speed=1.0,
+            )
+            return audio_result
+
+        def clear_conversation():
+            """Reset the conversation"""
+            return [], [
+                {
+                    "role": "system",
+                    "content": "You are not an AI assistant, you are whoever the user says you are. You must stay in character. Keep your responses concise since they will be spoken out loud.",
+                }
+            ]
+
+        def update_system_prompt(new_prompt):
+            """Update the system prompt and reset the conversation"""
+            new_conv_state = [{"role": "system", "content": new_prompt}]
+            return [], new_conv_state
+
+        # Handle audio input
+        audio_input_chat.stop_recording(
+            process_audio_input,
+            inputs=[audio_input_chat, chatbot_interface, conversation_state],
+            outputs=[chatbot_interface, conversation_state],
+        ).then(
+            generate_audio_response,
+            inputs=[chatbot_interface, ref_audio_chat, ref_text_chat, model_choice_chat, remove_silence_chat],
+            outputs=audio_output_chat,
+        )
+
+        # Handle clear button
+        clear_btn_chat.click(
+            clear_conversation,
+            outputs=[chatbot_interface, conversation_state],
+        )
+
+        # Handle system prompt change and reset conversation
+        system_prompt_chat.change(
+            update_system_prompt,
+            inputs=system_prompt_chat,
+            outputs=[chatbot_interface, conversation_state],
+        )
+
+
 with gr.Blocks() as app:
     gr.Markdown(
         """
@@ -509,7 +693,10 @@ If you're having issues, try converting your reference audio to WAV or MP3, clip
 **NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.**
 """
     )
-    gr.TabbedInterface([app_tts, app_podcast, app_emotional, app_credits], ["TTS", "Podcast", "Multi-Style", "Credits"])
+    gr.TabbedInterface(
+        [app_tts, app_podcast, app_emotional, app_chat, app_credits],
+        ["TTS", "Podcast", "Multi-Style", "Voice-Chat", "Credits"],
+    )
 
 
 @click.command()

data/Emilia_ZH_EN_pinyin/vocab.txt

@@ -1,2545 +1,2545 @@
- 
-!
-"
-#
-$
-%
-&
-'
-(
-)
-*
-+
-,
--
-.
-/
-0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-:
-;
-=
->
-?
-@
-A
-B
-C
-D
-E
-F
-G
-H
-I
-J
-K
-L
-M
-N
-O
-P
-Q
-R
-S
-T
-U
-V
-W
-X
-Y
-Z
-[
-\
-]
-_
-a
-a1
-ai1
-ai2
-ai3
-ai4
-an1
-an3
-an4
-ang1
-ang2
-ang4
-ao1
-ao2
-ao3
-ao4
-b
-ba
-ba1
-ba2
-ba3
-ba4
-bai1
-bai2
-bai3
-bai4
-ban1
-ban2
-ban3
-ban4
-bang1
-bang2
-bang3
-bang4
-bao1
-bao2
-bao3
-bao4
-bei
-bei1
-bei2
-bei3
-bei4
-ben1
-ben2
-ben3
-ben4
-beng
-beng1
-beng2
-beng3
-beng4
-bi1
-bi2
-bi3
-bi4
-bian1
-bian2
-bian3
-bian4
-biao1
-biao2
-biao3
-bie1
-bie2
-bie3
-bie4
-bin1
-bin4
-bing1
-bing2
-bing3
-bing4
-bo
-bo1
-bo2
-bo3
-bo4
-bu2
-bu3
-bu4
-c
-ca1
-cai1
-cai2
-cai3
-cai4
-can1
-can2
-can3
-can4
-cang1
-cang2
-cao1
-cao2
-cao3
-ce4
-cen1
-cen2
-ceng1
-ceng2
-ceng4
-cha1
-cha2
-cha3
-cha4
-chai1
-chai2
-chan1
-chan2
-chan3
-chan4
-chang1
-chang2
-chang3
-chang4
-chao1
-chao2
-chao3
-che1
-che2
-che3
-che4
-chen1
-chen2
-chen3
-chen4
-cheng1
-cheng2
-cheng3
-cheng4
-chi1
-chi2
-chi3
-chi4
-chong1
-chong2
-chong3
-chong4
-chou1
-chou2
-chou3
-chou4
-chu1
-chu2
-chu3
-chu4
-chua1
-chuai1
-chuai2
-chuai3
-chuai4
-chuan1
-chuan2
-chuan3
-chuan4
-chuang1
-chuang2
-chuang3
-chuang4
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+음
+응
+의
+이
+익
+인
+일
+읽
+임
+입
+있
+자
+작
+잔
+잖
+잘
+잡
+잤
+장
+재
+저
+전
+점
+정
+제
+져
+졌
+조
+족
+좀
+종
+좋
+죠
+주
+준
+줄
+중
+줘
+즈
+즐
+즘
+지
+진
+집
+짜
+짝
+쩌
+쪼
+쪽
+쫌
+쭈
+쯔
+찌
+찍
+차
+착
+찾
+책
+처
+천
+철
+체
+쳐
+쳤
+초
+촌
+추
+출
+춤
+춥
+춰
+치
+친
+칠
+침
+칩
+칼
+커
+켓
+코
+콩
+쿠
+퀴
+크
+큰
+큽
+키
+킨
+타
+태
+터
+턴
+털
+테
+토
+통
+투
+트
+특
+튼
+틀
+티
+팀
+파
+팔
+패
+페
+펜
+펭
+평
+포
+폭
+표
+품
+풍
+프
+플
+피
+필
+하
+학
+한
+할
+함
+합
+항
+해
+햇
+했
+행
+허
+험
+형
+혜
+호
+혼
+홀
+화
+회
+획
+후
+휴
+흐
+흔
+희
+히
+힘
+ﷺ
+ﷻ
+！
+，
+？
+�
+𠮶

pyproject.toml

@@ -0,0 +1,59 @@
+[build-system]
+requires = ["setuptools >= 61.0", "setuptools-scm>=8.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "f5-tts"
+dynamic = ["version"]
+description = "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
+readme = "README.md"
+license = {text = "MIT License"}
+classifiers = [
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+]
+dependencies = [
+    "accelerate>=0.33.0",
+    "bitsandbytes>0.37.0",
+    "cached_path",
+    "click",
+    "datasets",
+    "ema_pytorch>=0.5.2",
+    "gradio>=3.45.2",
+    "jieba",
+    "librosa",
+    "matplotlib",
+    "numpy<=1.26.4",
+    "pydub",
+    "pypinyin",
+    "safetensors",
+    "soundfile",
+    "tomli",
+    "torch>=2.0.0",
+    "torchaudio>=2.0.0",
+    "torchdiffeq",
+    "tqdm>=4.65.0",
+    "transformers",
+    "transformers_stream_generator",
+    "vocos",
+    "wandb",
+    "x_transformers>=1.31.14",
+]
+
+[project.optional-dependencies]
+eval = [
+    "faster_whisper==0.10.1",
+    "funasr",
+    "jiwer",
+    "modelscope",
+    "zhconv",
+    "zhon",
+]
+
+[project.urls]
+Homepage = "https://github.com/SWivid/F5-TTS"
+
+[project.scripts]
+"f5-tts_infer-cli" = "f5_tts.infer.infer_cli:main"
+"f5-tts_infer-gradio" = "f5_tts.infer.infer_gradio:main"

src/f5_tts/api.py

@@ -0,0 +1,138 @@
+import random
+import sys
+import tqdm
+from importlib.resources import files
+
+import soundfile as sf
+import torch
+from cached_path import cached_path
+
+from f5_tts.model import DiT, UNetT
+from f5_tts.model.utils import seed_everything
+from f5_tts.infer.utils_infer import (
+    load_vocoder,
+    load_model,
+    infer_process,
+    remove_silence_for_generated_wav,
+    save_spectrogram,
+)
+
+
+class F5TTS:
+    def __init__(
+        self,
+        model_type="F5-TTS",
+        ckpt_file="",
+        vocab_file="",
+        ode_method="euler",
+        use_ema=True,
+        local_path=None,
+        device=None,
+    ):
+        # Initialize parameters
+        self.final_wave = None
+        self.target_sample_rate = 24000
+        self.n_mel_channels = 100
+        self.hop_length = 256
+        self.target_rms = 0.1
+        self.seed = -1
+
+        # Set device
+        self.device = device or (
+            "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+        )
+
+        # Load models
+        self.load_vocoder_model(local_path)
+        self.load_ema_model(model_type, ckpt_file, vocab_file, ode_method, use_ema)
+
+    def load_vocoder_model(self, local_path):
+        self.vocos = load_vocoder(local_path is not None, local_path, self.device)
+
+    def load_ema_model(self, model_type, ckpt_file, vocab_file, ode_method, use_ema):
+        if model_type == "F5-TTS":
+            if not ckpt_file:
+                ckpt_file = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors"))
+            model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+            model_cls = DiT
+        elif model_type == "E2-TTS":
+            if not ckpt_file:
+                ckpt_file = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors"))
+            model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+            model_cls = UNetT
+        else:
+            raise ValueError(f"Unknown model type: {model_type}")
+
+        self.ema_model = load_model(model_cls, model_cfg, ckpt_file, vocab_file, ode_method, use_ema, self.device)
+
+    def export_wav(self, wav, file_wave, remove_silence=False):
+        sf.write(file_wave, wav, self.target_sample_rate)
+
+        if remove_silence:
+            remove_silence_for_generated_wav(file_wave)
+
+    def export_spectrogram(self, spect, file_spect):
+        save_spectrogram(spect, file_spect)
+
+    def infer(
+        self,
+        ref_file,
+        ref_text,
+        gen_text,
+        show_info=print,
+        progress=tqdm,
+        target_rms=0.1,
+        cross_fade_duration=0.15,
+        sway_sampling_coef=-1,
+        cfg_strength=2,
+        nfe_step=32,
+        speed=1.0,
+        fix_duration=None,
+        remove_silence=False,
+        file_wave=None,
+        file_spect=None,
+        seed=-1,
+    ):
+        if seed == -1:
+            seed = random.randint(0, sys.maxsize)
+        seed_everything(seed)
+        self.seed = seed
+        wav, sr, spect = infer_process(
+            ref_file,
+            ref_text,
+            gen_text,
+            self.ema_model,
+            show_info=show_info,
+            progress=progress,
+            target_rms=target_rms,
+            cross_fade_duration=cross_fade_duration,
+            nfe_step=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+            speed=speed,
+            fix_duration=fix_duration,
+            device=self.device,
+        )
+
+        if file_wave is not None:
+            self.export_wav(wav, file_wave, remove_silence)
+
+        if file_spect is not None:
+            self.export_spectrogram(spect, file_spect)
+
+        return wav, sr, spect
+
+
+if __name__ == "__main__":
+    f5tts = F5TTS()
+
+    wav, sr, spect = f5tts.infer(
+        ref_file=str(files("f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav")),
+        ref_text="some call me nature, others call me mother nature.",
+        gen_text="""I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.""",
+        file_wave=str(files("f5_tts").joinpath("../../tests/api_out.wav")),
+        file_spect=str(files("f5_tts").joinpath("../../tests/api_out.png")),
+        seed=-1,  # random seed = -1
+    )
+
+    print("seed :", f5tts.seed)

src/f5_tts/eval/README.md

@@ -0,0 +1,49 @@
+
+# Evaluation
+
+Install packages for evaluation:
+
+```bash
+pip install -e .[eval]
+```
+
+## Generating Samples for Evaluation
+
+### Prepare Test Datasets
+
+1. *Seed-TTS testset*: Download from [seed-tts-eval](https://github.com/BytedanceSpeech/seed-tts-eval).
+2. *LibriSpeech test-clean*: Download from [OpenSLR](http://www.openslr.org/12/).
+3. Unzip the downloaded datasets and place them in the `data/` directory.
+4. Update the path for *LibriSpeech test-clean* data in `src/f5_tts/eval/eval_infer_batch.py`
+5. Our filtered LibriSpeech-PC 4-10s subset: `data/librispeech_pc_test_clean_cross_sentence.lst`
+
+### Batch Inference for Test Set
+
+To run batch inference for evaluations, execute the following commands:
+
+```bash
+# batch inference for evaluations
+accelerate config  # if not set before
+bash src/f5_tts/eval/eval_infer_batch.sh
+```
+
+## Objective Evaluation on Generated Results
+
+### Download Evaluation Model Checkpoints
+
+1. Chinese ASR Model: [Paraformer-zh](https://huggingface.co/funasr/paraformer-zh)
+2. English ASR Model: [Faster-Whisper](https://huggingface.co/Systran/faster-whisper-large-v3)
+3. WavLM Model: Download from [Google Drive](https://drive.google.com/file/d/1-aE1NfzpRCLxA4GUxX9ITI3F9LlbtEGP/view).
+
+Then update in the following scripts with the paths you put evaluation model ckpts to.
+
+### Objective Evaluation
+
+Update the path with your batch-inferenced results, and carry out WER / SIM evaluations:
+```bash
+# Evaluation for Seed-TTS test set
+python src/f5_tts/eval/eval_seedtts_testset.py
+
+# Evaluation for LibriSpeech-PC test-clean (cross-sentence)
+python src/f5_tts/eval/eval_librispeech_test_clean.py
+```

src/f5_tts/eval/ecapa_tdnn.py

@@ -0,0 +1,330 @@
+# just for speaker similarity evaluation, third-party code
+
+# From https://github.com/microsoft/UniSpeech/blob/main/downstreams/speaker_verification/models/
+# part of the code is borrowed from https://github.com/lawlict/ECAPA-TDNN
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+""" Res2Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Res2Conv1dReluBn(nn.Module):
+    """
+    in_channels == out_channels == channels
+    """
+
+    def __init__(self, channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True, scale=4):
+        super().__init__()
+        assert channels % scale == 0, "{} % {} != 0".format(channels, scale)
+        self.scale = scale
+        self.width = channels // scale
+        self.nums = scale if scale == 1 else scale - 1
+
+        self.convs = []
+        self.bns = []
+        for i in range(self.nums):
+            self.convs.append(nn.Conv1d(self.width, self.width, kernel_size, stride, padding, dilation, bias=bias))
+            self.bns.append(nn.BatchNorm1d(self.width))
+        self.convs = nn.ModuleList(self.convs)
+        self.bns = nn.ModuleList(self.bns)
+
+    def forward(self, x):
+        out = []
+        spx = torch.split(x, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            # Order: conv -> relu -> bn
+            sp = self.convs[i](sp)
+            sp = self.bns[i](F.relu(sp))
+            out.append(sp)
+        if self.scale != 1:
+            out.append(spx[self.nums])
+        out = torch.cat(out, dim=1)
+
+        return out
+
+
+""" Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Conv1dReluBn(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True):
+        super().__init__()
+        self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)
+        self.bn = nn.BatchNorm1d(out_channels)
+
+    def forward(self, x):
+        return self.bn(F.relu(self.conv(x)))
+
+
+""" The SE connection of 1D case.
+"""
+
+
+class SE_Connect(nn.Module):
+    def __init__(self, channels, se_bottleneck_dim=128):
+        super().__init__()
+        self.linear1 = nn.Linear(channels, se_bottleneck_dim)
+        self.linear2 = nn.Linear(se_bottleneck_dim, channels)
+
+    def forward(self, x):
+        out = x.mean(dim=2)
+        out = F.relu(self.linear1(out))
+        out = torch.sigmoid(self.linear2(out))
+        out = x * out.unsqueeze(2)
+
+        return out
+
+
+""" SE-Res2Block of the ECAPA-TDNN architecture.
+"""
+
+# def SE_Res2Block(channels, kernel_size, stride, padding, dilation, scale):
+#     return nn.Sequential(
+#         Conv1dReluBn(channels, 512, kernel_size=1, stride=1, padding=0),
+#         Res2Conv1dReluBn(512, kernel_size, stride, padding, dilation, scale=scale),
+#         Conv1dReluBn(512, channels, kernel_size=1, stride=1, padding=0),
+#         SE_Connect(channels)
+#     )
+
+
+class SE_Res2Block(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, scale, se_bottleneck_dim):
+        super().__init__()
+        self.Conv1dReluBn1 = Conv1dReluBn(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        self.Res2Conv1dReluBn = Res2Conv1dReluBn(out_channels, kernel_size, stride, padding, dilation, scale=scale)
+        self.Conv1dReluBn2 = Conv1dReluBn(out_channels, out_channels, kernel_size=1, stride=1, padding=0)
+        self.SE_Connect = SE_Connect(out_channels, se_bottleneck_dim)
+
+        self.shortcut = None
+        if in_channels != out_channels:
+            self.shortcut = nn.Conv1d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+            )
+
+    def forward(self, x):
+        residual = x
+        if self.shortcut:
+            residual = self.shortcut(x)
+
+        x = self.Conv1dReluBn1(x)
+        x = self.Res2Conv1dReluBn(x)
+        x = self.Conv1dReluBn2(x)
+        x = self.SE_Connect(x)
+
+        return x + residual
+
+
+""" Attentive weighted mean and standard deviation pooling.
+"""
+
+
+class AttentiveStatsPool(nn.Module):
+    def __init__(self, in_dim, attention_channels=128, global_context_att=False):
+        super().__init__()
+        self.global_context_att = global_context_att
+
+        # Use Conv1d with stride == 1 rather than Linear, then we don't need to transpose inputs.
+        if global_context_att:
+            self.linear1 = nn.Conv1d(in_dim * 3, attention_channels, kernel_size=1)  # equals W and b in the paper
+        else:
+            self.linear1 = nn.Conv1d(in_dim, attention_channels, kernel_size=1)  # equals W and b in the paper
+        self.linear2 = nn.Conv1d(attention_channels, in_dim, kernel_size=1)  # equals V and k in the paper
+
+    def forward(self, x):
+        if self.global_context_att:
+            context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
+            context_std = torch.sqrt(torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
+            x_in = torch.cat((x, context_mean, context_std), dim=1)
+        else:
+            x_in = x
+
+        # DON'T use ReLU here! In experiments, I find ReLU hard to converge.
+        alpha = torch.tanh(self.linear1(x_in))
+        # alpha = F.relu(self.linear1(x_in))
+        alpha = torch.softmax(self.linear2(alpha), dim=2)
+        mean = torch.sum(alpha * x, dim=2)
+        residuals = torch.sum(alpha * (x**2), dim=2) - mean**2
+        std = torch.sqrt(residuals.clamp(min=1e-9))
+        return torch.cat([mean, std], dim=1)
+
+
+class ECAPA_TDNN(nn.Module):
+    def __init__(
+        self,
+        feat_dim=80,
+        channels=512,
+        emb_dim=192,
+        global_context_att=False,
+        feat_type="wavlm_large",
+        sr=16000,
+        feature_selection="hidden_states",
+        update_extract=False,
+        config_path=None,
+    ):
+        super().__init__()
+
+        self.feat_type = feat_type
+        self.feature_selection = feature_selection
+        self.update_extract = update_extract
+        self.sr = sr
+
+        torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
+        try:
+            local_s3prl_path = os.path.expanduser("~/.cache/torch/hub/s3prl_s3prl_main")
+            self.feature_extract = torch.hub.load(local_s3prl_path, feat_type, source="local", config_path=config_path)
+        except:  # noqa: E722
+            self.feature_extract = torch.hub.load("s3prl/s3prl", feat_type)
+
+        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
+            self.feature_extract.model.encoder.layers[23].self_attn, "fp32_attention"
+        ):
+            self.feature_extract.model.encoder.layers[23].self_attn.fp32_attention = False
+        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
+            self.feature_extract.model.encoder.layers[11].self_attn, "fp32_attention"
+        ):
+            self.feature_extract.model.encoder.layers[11].self_attn.fp32_attention = False
+
+        self.feat_num = self.get_feat_num()
+        self.feature_weight = nn.Parameter(torch.zeros(self.feat_num))
+
+        if feat_type != "fbank" and feat_type != "mfcc":
+            freeze_list = ["final_proj", "label_embs_concat", "mask_emb", "project_q", "quantizer"]
+            for name, param in self.feature_extract.named_parameters():
+                for freeze_val in freeze_list:
+                    if freeze_val in name:
+                        param.requires_grad = False
+                        break
+
+        if not self.update_extract:
+            for param in self.feature_extract.parameters():
+                param.requires_grad = False
+
+        self.instance_norm = nn.InstanceNorm1d(feat_dim)
+        # self.channels = [channels] * 4 + [channels * 3]
+        self.channels = [channels] * 4 + [1536]
+
+        self.layer1 = Conv1dReluBn(feat_dim, self.channels[0], kernel_size=5, padding=2)
+        self.layer2 = SE_Res2Block(
+            self.channels[0],
+            self.channels[1],
+            kernel_size=3,
+            stride=1,
+            padding=2,
+            dilation=2,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+        self.layer3 = SE_Res2Block(
+            self.channels[1],
+            self.channels[2],
+            kernel_size=3,
+            stride=1,
+            padding=3,
+            dilation=3,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+        self.layer4 = SE_Res2Block(
+            self.channels[2],
+            self.channels[3],
+            kernel_size=3,
+            stride=1,
+            padding=4,
+            dilation=4,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+
+        # self.conv = nn.Conv1d(self.channels[-1], self.channels[-1], kernel_size=1)
+        cat_channels = channels * 3
+        self.conv = nn.Conv1d(cat_channels, self.channels[-1], kernel_size=1)
+        self.pooling = AttentiveStatsPool(
+            self.channels[-1], attention_channels=128, global_context_att=global_context_att
+        )
+        self.bn = nn.BatchNorm1d(self.channels[-1] * 2)
+        self.linear = nn.Linear(self.channels[-1] * 2, emb_dim)
+
+    def get_feat_num(self):
+        self.feature_extract.eval()
+        wav = [torch.randn(self.sr).to(next(self.feature_extract.parameters()).device)]
+        with torch.no_grad():
+            features = self.feature_extract(wav)
+        select_feature = features[self.feature_selection]
+        if isinstance(select_feature, (list, tuple)):
+            return len(select_feature)
+        else:
+            return 1
+
+    def get_feat(self, x):
+        if self.update_extract:
+            x = self.feature_extract([sample for sample in x])
+        else:
+            with torch.no_grad():
+                if self.feat_type == "fbank" or self.feat_type == "mfcc":
+                    x = self.feature_extract(x) + 1e-6  # B x feat_dim x time_len
+                else:
+                    x = self.feature_extract([sample for sample in x])
+
+        if self.feat_type == "fbank":
+            x = x.log()
+
+        if self.feat_type != "fbank" and self.feat_type != "mfcc":
+            x = x[self.feature_selection]
+            if isinstance(x, (list, tuple)):
+                x = torch.stack(x, dim=0)
+            else:
+                x = x.unsqueeze(0)
+            norm_weights = F.softmax(self.feature_weight, dim=-1).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
+            x = (norm_weights * x).sum(dim=0)
+            x = torch.transpose(x, 1, 2) + 1e-6
+
+        x = self.instance_norm(x)
+        return x
+
+    def forward(self, x):
+        x = self.get_feat(x)
+
+        out1 = self.layer1(x)
+        out2 = self.layer2(out1)
+        out3 = self.layer3(out2)
+        out4 = self.layer4(out3)
+
+        out = torch.cat([out2, out3, out4], dim=1)
+        out = F.relu(self.conv(out))
+        out = self.bn(self.pooling(out))
+        out = self.linear(out)
+
+        return out
+
+
+def ECAPA_TDNN_SMALL(
+    feat_dim,
+    emb_dim=256,
+    feat_type="wavlm_large",
+    sr=16000,
+    feature_selection="hidden_states",
+    update_extract=False,
+    config_path=None,
+):
+    return ECAPA_TDNN(
+        feat_dim=feat_dim,
+        channels=512,
+        emb_dim=emb_dim,
+        feat_type=feat_type,
+        sr=sr,
+        feature_selection=feature_selection,
+        update_extract=update_extract,
+        config_path=config_path,
+    )

src/f5_tts/eval/eval_infer_batch.py

@@ -0,0 +1,197 @@
+import sys
+import os
+
+sys.path.append(os.getcwd())
+
+import time
+from tqdm import tqdm
+import argparse
+from importlib.resources import files
+
+import torch
+import torchaudio
+from accelerate import Accelerator
+from vocos import Vocos
+
+from f5_tts.model import CFM, UNetT, DiT
+from f5_tts.model.utils import get_tokenizer
+from f5_tts.infer.utils_infer import load_checkpoint
+from f5_tts.eval.utils_eval import (
+    get_seedtts_testset_metainfo,
+    get_librispeech_test_clean_metainfo,
+    get_inference_prompt,
+)
+
+accelerator = Accelerator()
+device = f"cuda:{accelerator.process_index}"
+
+
+# --------------------- Dataset Settings -------------------- #
+
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+target_rms = 0.1
+
+tokenizer = "pinyin"
+rel_path = str(files("f5_tts").joinpath("../../"))
+
+
+def main():
+    # ---------------------- infer setting ---------------------- #
+
+    parser = argparse.ArgumentParser(description="batch inference")
+
+    parser.add_argument("-s", "--seed", default=None, type=int)
+    parser.add_argument("-d", "--dataset", default="Emilia_ZH_EN")
+    parser.add_argument("-n", "--expname", required=True)
+    parser.add_argument("-c", "--ckptstep", default=1200000, type=int)
+
+    parser.add_argument("-nfe", "--nfestep", default=32, type=int)
+    parser.add_argument("-o", "--odemethod", default="euler")
+    parser.add_argument("-ss", "--swaysampling", default=-1, type=float)
+
+    parser.add_argument("-t", "--testset", required=True)
+
+    args = parser.parse_args()
+
+    seed = args.seed
+    dataset_name = args.dataset
+    exp_name = args.expname
+    ckpt_step = args.ckptstep
+    ckpt_path = rel_path + f"/ckpts/{exp_name}/model_{ckpt_step}.pt"
+
+    nfe_step = args.nfestep
+    ode_method = args.odemethod
+    sway_sampling_coef = args.swaysampling
+
+    testset = args.testset
+
+    infer_batch_size = 1  # max frames. 1 for ddp single inference (recommended)
+    cfg_strength = 2.0
+    speed = 1.0
+    use_truth_duration = False
+    no_ref_audio = False
+
+    if exp_name == "F5TTS_Base":
+        model_cls = DiT
+        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+
+    elif exp_name == "E2TTS_Base":
+        model_cls = UNetT
+        model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+
+    if testset == "ls_pc_test_clean":
+        metalst = rel_path + "/data/librispeech_pc_test_clean_cross_sentence.lst"
+        librispeech_test_clean_path = "<SOME_PATH>/LibriSpeech/test-clean"  # test-clean path
+        metainfo = get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path)
+
+    elif testset == "seedtts_test_zh":
+        metalst = rel_path + "/data/seedtts_testset/zh/meta.lst"
+        metainfo = get_seedtts_testset_metainfo(metalst)
+
+    elif testset == "seedtts_test_en":
+        metalst = rel_path + "/data/seedtts_testset/en/meta.lst"
+        metainfo = get_seedtts_testset_metainfo(metalst)
+
+    # path to save genereted wavs
+    output_dir = (
+        f"{rel_path}/"
+        f"results/{exp_name}_{ckpt_step}/{testset}/"
+        f"seed{seed}_{ode_method}_nfe{nfe_step}"
+        f"{f'_ss{sway_sampling_coef}' if sway_sampling_coef else ''}"
+        f"_cfg{cfg_strength}_speed{speed}"
+        f"{'_gt-dur' if use_truth_duration else ''}"
+        f"{'_no-ref-audio' if no_ref_audio else ''}"
+    )
+
+    # -------------------------------------------------#
+
+    use_ema = True
+
+    prompts_all = get_inference_prompt(
+        metainfo,
+        speed=speed,
+        tokenizer=tokenizer,
+        target_sample_rate=target_sample_rate,
+        n_mel_channels=n_mel_channels,
+        hop_length=hop_length,
+        target_rms=target_rms,
+        use_truth_duration=use_truth_duration,
+        infer_batch_size=infer_batch_size,
+    )
+
+    # Vocoder model
+    local = False
+    if local:
+        vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
+        vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml")
+        state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", weights_only=True, map_location=device)
+        vocos.load_state_dict(state_dict)
+        vocos.eval()
+    else:
+        vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+
+    # Tokenizer
+    vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer)
+
+    # Model
+    model = CFM(
+        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=dict(
+            target_sample_rate=target_sample_rate,
+            n_mel_channels=n_mel_channels,
+            hop_length=hop_length,
+        ),
+        odeint_kwargs=dict(
+            method=ode_method,
+        ),
+        vocab_char_map=vocab_char_map,
+    ).to(device)
+
+    model = load_checkpoint(model, ckpt_path, device, use_ema=use_ema)
+
+    if not os.path.exists(output_dir) and accelerator.is_main_process:
+        os.makedirs(output_dir)
+
+    # start batch inference
+    accelerator.wait_for_everyone()
+    start = time.time()
+
+    with accelerator.split_between_processes(prompts_all) as prompts:
+        for prompt in tqdm(prompts, disable=not accelerator.is_local_main_process):
+            utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = prompt
+            ref_mels = ref_mels.to(device)
+            ref_mel_lens = torch.tensor(ref_mel_lens, dtype=torch.long).to(device)
+            total_mel_lens = torch.tensor(total_mel_lens, dtype=torch.long).to(device)
+
+            # Inference
+            with torch.inference_mode():
+                generated, _ = model.sample(
+                    cond=ref_mels,
+                    text=final_text_list,
+                    duration=total_mel_lens,
+                    lens=ref_mel_lens,
+                    steps=nfe_step,
+                    cfg_strength=cfg_strength,
+                    sway_sampling_coef=sway_sampling_coef,
+                    no_ref_audio=no_ref_audio,
+                    seed=seed,
+                )
+            # Final result
+            for i, gen in enumerate(generated):
+                gen = gen[ref_mel_lens[i] : total_mel_lens[i], :].unsqueeze(0)
+                gen_mel_spec = gen.permute(0, 2, 1)
+                generated_wave = vocos.decode(gen_mel_spec.cpu())
+                if ref_rms_list[i] < target_rms:
+                    generated_wave = generated_wave * ref_rms_list[i] / target_rms
+                torchaudio.save(f"{output_dir}/{utts[i]}.wav", generated_wave, target_sample_rate)
+
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        timediff = time.time() - start
+        print(f"Done batch inference in {timediff / 60 :.2f} minutes.")
+
+
+if __name__ == "__main__":
+    main()

src/f5_tts/eval/eval_infer_batch.sh

@@ -0,0 +1,13 @@
+#!/bin/bash
+
+# e.g. F5-TTS, 16 NFE
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_Base" -t "seedtts_test_zh" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_Base" -t "seedtts_test_en" -nfe 16
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "F5TTS_Base" -t "ls_pc_test_clean" -nfe 16
+
+# e.g. Vanilla E2 TTS, 32 NFE
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -t "seedtts_test_zh" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -t "seedtts_test_en" -o "midpoint" -ss 0
+accelerate launch src/f5_tts/eval/eval_infer_batch.py -s 0 -n "E2TTS_Base" -t "ls_pc_test_clean" -o "midpoint" -ss 0
+
+# etc.

src/f5_tts/eval/eval_librispeech_test_clean.py

@@ -0,0 +1,73 @@
+# Evaluate with Librispeech test-clean, ~3s prompt to generate 4-10s audio (the way of valle/voicebox evaluation)
+
+import sys
+import os
+
+sys.path.append(os.getcwd())
+
+import multiprocessing as mp
+from importlib.resources import files
+
+import numpy as np
+
+from f5_tts.eval.utils_eval import (
+    get_librispeech_test,
+    run_asr_wer,
+    run_sim,
+)
+
+rel_path = str(files("f5_tts").joinpath("../../"))
+
+
+eval_task = "wer"  # sim | wer
+lang = "en"
+metalst = rel_path + "/data/librispeech_pc_test_clean_cross_sentence.lst"
+librispeech_test_clean_path = "<SOME_PATH>/LibriSpeech/test-clean"  # test-clean path
+gen_wav_dir = "PATH_TO_GENERATED"  # generated wavs
+
+gpus = [0, 1, 2, 3, 4, 5, 6, 7]
+test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path)
+
+## In LibriSpeech, some speakers utilized varying voice characteristics for different characters in the book,
+## leading to a low similarity for the ground truth in some cases.
+# test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth = True)  # eval ground truth
+
+local = False
+if local:  # use local custom checkpoint dir
+    asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3"
+else:
+    asr_ckpt_dir = ""  # auto download to cache dir
+
+wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
+
+
+# --------------------------- WER ---------------------------
+
+if eval_task == "wer":
+    wers = []
+
+    with mp.Pool(processes=len(gpus)) as pool:
+        args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
+        results = pool.map(run_asr_wer, args)
+        for wers_ in results:
+            wers.extend(wers_)
+
+    wer = round(np.mean(wers) * 100, 3)
+    print(f"\nTotal {len(wers)} samples")
+    print(f"WER      : {wer}%")
+
+
+# --------------------------- SIM ---------------------------
+
+if eval_task == "sim":
+    sim_list = []
+
+    with mp.Pool(processes=len(gpus)) as pool:
+        args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
+        results = pool.map(run_sim, args)
+        for sim_ in results:
+            sim_list.extend(sim_)
+
+    sim = round(sum(sim_list) / len(sim_list), 3)
+    print(f"\nTotal {len(sim_list)} samples")
+    print(f"SIM      : {sim}")

src/f5_tts/eval/eval_seedtts_testset.py

@@ -0,0 +1,75 @@
+# Evaluate with Seed-TTS testset
+
+import sys
+import os
+
+sys.path.append(os.getcwd())
+
+import multiprocessing as mp
+from importlib.resources import files
+
+import numpy as np
+
+from f5_tts.eval.utils_eval import (
+    get_seed_tts_test,
+    run_asr_wer,
+    run_sim,
+)
+
+rel_path = str(files("f5_tts").joinpath("../../"))
+
+
+eval_task = "wer"  # sim | wer
+lang = "zh"  # zh | en
+metalst = rel_path + f"/data/seedtts_testset/{lang}/meta.lst"  # seed-tts testset
+# gen_wav_dir = rel_path + f"/data/seedtts_testset/{lang}/wavs"  # ground truth wavs
+gen_wav_dir = "PATH_TO_GENERATED"  # generated wavs
+
+
+# NOTE. paraformer-zh result will be slightly different according to the number of gpus, cuz batchsize is different
+#       zh 1.254 seems a result of 4 workers wer_seed_tts
+gpus = [0, 1, 2, 3, 4, 5, 6, 7]
+test_set = get_seed_tts_test(metalst, gen_wav_dir, gpus)
+
+local = False
+if local:  # use local custom checkpoint dir
+    if lang == "zh":
+        asr_ckpt_dir = "../checkpoints/funasr"  # paraformer-zh dir under funasr
+    elif lang == "en":
+        asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3"
+else:
+    asr_ckpt_dir = ""  # auto download to cache dir
+
+wavlm_ckpt_dir = "../checkpoints/UniSpeech/wavlm_large_finetune.pth"
+
+
+# --------------------------- WER ---------------------------
+
+if eval_task == "wer":
+    wers = []
+
+    with mp.Pool(processes=len(gpus)) as pool:
+        args = [(rank, lang, sub_test_set, asr_ckpt_dir) for (rank, sub_test_set) in test_set]
+        results = pool.map(run_asr_wer, args)
+        for wers_ in results:
+            wers.extend(wers_)
+
+    wer = round(np.mean(wers) * 100, 3)
+    print(f"\nTotal {len(wers)} samples")
+    print(f"WER      : {wer}%")
+
+
+# --------------------------- SIM ---------------------------
+
+if eval_task == "sim":
+    sim_list = []
+
+    with mp.Pool(processes=len(gpus)) as pool:
+        args = [(rank, sub_test_set, wavlm_ckpt_dir) for (rank, sub_test_set) in test_set]
+        results = pool.map(run_sim, args)
+        for sim_ in results:
+            sim_list.extend(sim_)
+
+    sim = round(sum(sim_list) / len(sim_list), 3)
+    print(f"\nTotal {len(sim_list)} samples")
+    print(f"SIM      : {sim}")

src/f5_tts/eval/utils_eval.py

@@ -0,0 +1,397 @@
+import math
+import os
+import random
+import string
+from tqdm import tqdm
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+
+from f5_tts.model.modules import MelSpec
+from f5_tts.model.utils import convert_char_to_pinyin
+from f5_tts.eval.ecapa_tdnn import ECAPA_TDNN_SMALL
+
+
+# seedtts testset metainfo: utt, prompt_text, prompt_wav, gt_text, gt_wav
+def get_seedtts_testset_metainfo(metalst):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+    metainfo = []
+    for line in lines:
+        if len(line.strip().split("|")) == 5:
+            utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
+        elif len(line.strip().split("|")) == 4:
+            utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
+            gt_wav = os.path.join(os.path.dirname(metalst), "wavs", utt + ".wav")
+        if not os.path.isabs(prompt_wav):
+            prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
+        metainfo.append((utt, prompt_text, prompt_wav, gt_text, gt_wav))
+    return metainfo
+
+
+# librispeech test-clean metainfo: gen_utt, ref_txt, ref_wav, gen_txt, gen_wav
+def get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+    metainfo = []
+    for line in lines:
+        ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
+
+        # ref_txt = ref_txt[0] + ref_txt[1:].lower() + '.'  # if use librispeech test-clean (no-pc)
+        ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
+        ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
+
+        # gen_txt = gen_txt[0] + gen_txt[1:].lower() + '.'  # if use librispeech test-clean (no-pc)
+        gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
+        gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
+
+        metainfo.append((gen_utt, ref_txt, ref_wav, " " + gen_txt, gen_wav))
+
+    return metainfo
+
+
+# padded to max length mel batch
+def padded_mel_batch(ref_mels):
+    max_mel_length = torch.LongTensor([mel.shape[-1] for mel in ref_mels]).amax()
+    padded_ref_mels = []
+    for mel in ref_mels:
+        padded_ref_mel = F.pad(mel, (0, max_mel_length - mel.shape[-1]), value=0)
+        padded_ref_mels.append(padded_ref_mel)
+    padded_ref_mels = torch.stack(padded_ref_mels)
+    padded_ref_mels = padded_ref_mels.permute(0, 2, 1)
+    return padded_ref_mels
+
+
+# get prompts from metainfo containing: utt, prompt_text, prompt_wav, gt_text, gt_wav
+
+
+def get_inference_prompt(
+    metainfo,
+    speed=1.0,
+    tokenizer="pinyin",
+    polyphone=True,
+    target_sample_rate=24000,
+    n_mel_channels=100,
+    hop_length=256,
+    target_rms=0.1,
+    use_truth_duration=False,
+    infer_batch_size=1,
+    num_buckets=200,
+    min_secs=3,
+    max_secs=40,
+):
+    prompts_all = []
+
+    min_tokens = min_secs * target_sample_rate // hop_length
+    max_tokens = max_secs * target_sample_rate // hop_length
+
+    batch_accum = [0] * num_buckets
+    utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = (
+        [[] for _ in range(num_buckets)] for _ in range(6)
+    )
+
+    mel_spectrogram = MelSpec(
+        target_sample_rate=target_sample_rate, n_mel_channels=n_mel_channels, hop_length=hop_length
+    )
+
+    for utt, prompt_text, prompt_wav, gt_text, gt_wav in tqdm(metainfo, desc="Processing prompts..."):
+        # Audio
+        ref_audio, ref_sr = torchaudio.load(prompt_wav)
+        ref_rms = torch.sqrt(torch.mean(torch.square(ref_audio)))
+        if ref_rms < target_rms:
+            ref_audio = ref_audio * target_rms / ref_rms
+        assert ref_audio.shape[-1] > 5000, f"Empty prompt wav: {prompt_wav}, or torchaudio backend issue."
+        if ref_sr != target_sample_rate:
+            resampler = torchaudio.transforms.Resample(ref_sr, target_sample_rate)
+            ref_audio = resampler(ref_audio)
+
+        # Text
+        if len(prompt_text[-1].encode("utf-8")) == 1:
+            prompt_text = prompt_text + " "
+        text = [prompt_text + gt_text]
+        if tokenizer == "pinyin":
+            text_list = convert_char_to_pinyin(text, polyphone=polyphone)
+        else:
+            text_list = text
+
+        # Duration, mel frame length
+        ref_mel_len = ref_audio.shape[-1] // hop_length
+        if use_truth_duration:
+            gt_audio, gt_sr = torchaudio.load(gt_wav)
+            if gt_sr != target_sample_rate:
+                resampler = torchaudio.transforms.Resample(gt_sr, target_sample_rate)
+                gt_audio = resampler(gt_audio)
+            total_mel_len = ref_mel_len + int(gt_audio.shape[-1] / hop_length / speed)
+
+            # # test vocoder resynthesis
+            # ref_audio = gt_audio
+        else:
+            ref_text_len = len(prompt_text.encode("utf-8"))
+            gen_text_len = len(gt_text.encode("utf-8"))
+            total_mel_len = ref_mel_len + int(ref_mel_len / ref_text_len * gen_text_len / speed)
+
+        # to mel spectrogram
+        ref_mel = mel_spectrogram(ref_audio)
+        ref_mel = ref_mel.squeeze(0)
+
+        # deal with batch
+        assert infer_batch_size > 0, "infer_batch_size should be greater than 0."
+        assert (
+            min_tokens <= total_mel_len <= max_tokens
+        ), f"Audio {utt} has duration {total_mel_len*hop_length//target_sample_rate}s out of range [{min_secs}, {max_secs}]."
+        bucket_i = math.floor((total_mel_len - min_tokens) / (max_tokens - min_tokens + 1) * num_buckets)
+
+        utts[bucket_i].append(utt)
+        ref_rms_list[bucket_i].append(ref_rms)
+        ref_mels[bucket_i].append(ref_mel)
+        ref_mel_lens[bucket_i].append(ref_mel_len)
+        total_mel_lens[bucket_i].append(total_mel_len)
+        final_text_list[bucket_i].extend(text_list)
+
+        batch_accum[bucket_i] += total_mel_len
+
+        if batch_accum[bucket_i] >= infer_batch_size:
+            # print(f"\n{len(ref_mels[bucket_i][0][0])}\n{ref_mel_lens[bucket_i]}\n{total_mel_lens[bucket_i]}")
+            prompts_all.append(
+                (
+                    utts[bucket_i],
+                    ref_rms_list[bucket_i],
+                    padded_mel_batch(ref_mels[bucket_i]),
+                    ref_mel_lens[bucket_i],
+                    total_mel_lens[bucket_i],
+                    final_text_list[bucket_i],
+                )
+            )
+            batch_accum[bucket_i] = 0
+            (
+                utts[bucket_i],
+                ref_rms_list[bucket_i],
+                ref_mels[bucket_i],
+                ref_mel_lens[bucket_i],
+                total_mel_lens[bucket_i],
+                final_text_list[bucket_i],
+            ) = [], [], [], [], [], []
+
+    # add residual
+    for bucket_i, bucket_frames in enumerate(batch_accum):
+        if bucket_frames > 0:
+            prompts_all.append(
+                (
+                    utts[bucket_i],
+                    ref_rms_list[bucket_i],
+                    padded_mel_batch(ref_mels[bucket_i]),
+                    ref_mel_lens[bucket_i],
+                    total_mel_lens[bucket_i],
+                    final_text_list[bucket_i],
+                )
+            )
+    # not only leave easy work for last workers
+    random.seed(666)
+    random.shuffle(prompts_all)
+
+    return prompts_all
+
+
+# get wav_res_ref_text of seed-tts test metalst
+# https://github.com/BytedanceSpeech/seed-tts-eval
+
+
+def get_seed_tts_test(metalst, gen_wav_dir, gpus):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+
+    test_set_ = []
+    for line in tqdm(lines):
+        if len(line.strip().split("|")) == 5:
+            utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|")
+        elif len(line.strip().split("|")) == 4:
+            utt, prompt_text, prompt_wav, gt_text = line.strip().split("|")
+
+        if not os.path.exists(os.path.join(gen_wav_dir, utt + ".wav")):
+            continue
+        gen_wav = os.path.join(gen_wav_dir, utt + ".wav")
+        if not os.path.isabs(prompt_wav):
+            prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav)
+
+        test_set_.append((gen_wav, prompt_wav, gt_text))
+
+    num_jobs = len(gpus)
+    if num_jobs == 1:
+        return [(gpus[0], test_set_)]
+
+    wav_per_job = len(test_set_) // num_jobs + 1
+    test_set = []
+    for i in range(num_jobs):
+        test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
+
+    return test_set
+
+
+# get librispeech test-clean cross sentence test
+
+
+def get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth=False):
+    f = open(metalst)
+    lines = f.readlines()
+    f.close()
+
+    test_set_ = []
+    for line in tqdm(lines):
+        ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t")
+
+        if eval_ground_truth:
+            gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-")
+            gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac")
+        else:
+            if not os.path.exists(os.path.join(gen_wav_dir, gen_utt + ".wav")):
+                raise FileNotFoundError(f"Generated wav not found: {gen_utt}")
+            gen_wav = os.path.join(gen_wav_dir, gen_utt + ".wav")
+
+        ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-")
+        ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac")
+
+        test_set_.append((gen_wav, ref_wav, gen_txt))
+
+    num_jobs = len(gpus)
+    if num_jobs == 1:
+        return [(gpus[0], test_set_)]
+
+    wav_per_job = len(test_set_) // num_jobs + 1
+    test_set = []
+    for i in range(num_jobs):
+        test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job]))
+
+    return test_set
+
+
+# load asr model
+
+
+def load_asr_model(lang, ckpt_dir=""):
+    if lang == "zh":
+        from funasr import AutoModel
+
+        model = AutoModel(
+            model=os.path.join(ckpt_dir, "paraformer-zh"),
+            # vad_model = os.path.join(ckpt_dir, "fsmn-vad"),
+            # punc_model = os.path.join(ckpt_dir, "ct-punc"),
+            # spk_model = os.path.join(ckpt_dir, "cam++"),
+            disable_update=True,
+        )  # following seed-tts setting
+    elif lang == "en":
+        from faster_whisper import WhisperModel
+
+        model_size = "large-v3" if ckpt_dir == "" else ckpt_dir
+        model = WhisperModel(model_size, device="cuda", compute_type="float16")
+    return model
+
+
+# WER Evaluation, the way Seed-TTS does
+
+
+def run_asr_wer(args):
+    rank, lang, test_set, ckpt_dir = args
+
+    if lang == "zh":
+        import zhconv
+
+        torch.cuda.set_device(rank)
+    elif lang == "en":
+        os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
+    else:
+        raise NotImplementedError(
+            "lang support only 'zh' (funasr paraformer-zh), 'en' (faster-whisper-large-v3), for now."
+        )
+
+    asr_model = load_asr_model(lang, ckpt_dir=ckpt_dir)
+
+    from zhon.hanzi import punctuation
+
+    punctuation_all = punctuation + string.punctuation
+    wers = []
+
+    from jiwer import compute_measures
+
+    for gen_wav, prompt_wav, truth in tqdm(test_set):
+        if lang == "zh":
+            res = asr_model.generate(input=gen_wav, batch_size_s=300, disable_pbar=True)
+            hypo = res[0]["text"]
+            hypo = zhconv.convert(hypo, "zh-cn")
+        elif lang == "en":
+            segments, _ = asr_model.transcribe(gen_wav, beam_size=5, language="en")
+            hypo = ""
+            for segment in segments:
+                hypo = hypo + " " + segment.text
+
+        # raw_truth = truth
+        # raw_hypo = hypo
+
+        for x in punctuation_all:
+            truth = truth.replace(x, "")
+            hypo = hypo.replace(x, "")
+
+        truth = truth.replace("  ", " ")
+        hypo = hypo.replace("  ", " ")
+
+        if lang == "zh":
+            truth = " ".join([x for x in truth])
+            hypo = " ".join([x for x in hypo])
+        elif lang == "en":
+            truth = truth.lower()
+            hypo = hypo.lower()
+
+        measures = compute_measures(truth, hypo)
+        wer = measures["wer"]
+
+        # ref_list = truth.split(" ")
+        # subs = measures["substitutions"] / len(ref_list)
+        # dele = measures["deletions"] / len(ref_list)
+        # inse = measures["insertions"] / len(ref_list)
+
+        wers.append(wer)
+
+    return wers
+
+
+# SIM Evaluation
+
+
+def run_sim(args):
+    rank, test_set, ckpt_dir = args
+    device = f"cuda:{rank}"
+
+    model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type="wavlm_large", config_path=None)
+    state_dict = torch.load(ckpt_dir, weights_only=True, map_location=lambda storage, loc: storage)
+    model.load_state_dict(state_dict["model"], strict=False)
+
+    use_gpu = True if torch.cuda.is_available() else False
+    if use_gpu:
+        model = model.cuda(device)
+    model.eval()
+
+    sim_list = []
+    for wav1, wav2, truth in tqdm(test_set):
+        wav1, sr1 = torchaudio.load(wav1)
+        wav2, sr2 = torchaudio.load(wav2)
+
+        resample1 = torchaudio.transforms.Resample(orig_freq=sr1, new_freq=16000)
+        resample2 = torchaudio.transforms.Resample(orig_freq=sr2, new_freq=16000)
+        wav1 = resample1(wav1)
+        wav2 = resample2(wav2)
+
+        if use_gpu:
+            wav1 = wav1.cuda(device)
+            wav2 = wav2.cuda(device)
+        with torch.no_grad():
+            emb1 = model(wav1)
+            emb2 = model(wav2)
+
+        sim = F.cosine_similarity(emb1, emb2)[0].item()
+        # print(f"VSim score between two audios: {sim:.4f} (-1.0, 1.0).")
+        sim_list.append(sim)
+
+    return sim_list

src/f5_tts/infer/README.md

@@ -0,0 +1,111 @@
+# Inference
+
+The pretrained model checkpoints can be reached at [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS) and [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), or will be automatically downloaded when running inference scripts.
+
+Currently support **30s for a single** generation, which is the **total length** including both prompt and output audio. However, you can provide `infer_cli` and `infer_gradio` with longer text, will automatically do chunk generation. Long reference audio will be **clip short to ~15s**.
+
+To avoid possible inference failures, make sure you have seen through the following instructions.
+
+- Uppercased letters will be uttered letter by letter, so use lowercased letters for normal words. 
+- Add some spaces (blank: " ") or punctuations (e.g. "," ".") to explicitly introduce some pauses.
+- Preprocess numbers to Chinese letters if you want to have them read in Chinese, otherwise in English.
+
+
+## Gradio App
+
+Currently supported features:
+
+- Basic TTS with Chunk Inference
+- Multi-Style / Multi-Speaker Generation
+- Voice Chat powered by Qwen2.5-3B-Instruct
+
+The cli command `f5-tts_infer-gradio` equals to `python src/f5_tts/infer/infer_gradio.py`, which launches a Gradio APP (web interface) for inference.
+
+The script will load model checkpoints from Huggingface. You can also manually download files and update the path to `load_model()` in `infer_gradio.py`. Currently only load TTS models first, will load ASR model to do transcription if `ref_text` not provided, will load LLM model if use Voice Chat.
+
+Could also be used as a component for larger application.
+```python
+import gradio as gr
+from f5_tts.infer.infer_gradio import app
+
+with gr.Blocks() as main_app:
+    gr.Markdown("# This is an example of using F5-TTS within a bigger Gradio app")
+
+    # ... other Gradio components
+
+    app.render()
+
+main_app.launch()
+```
+
+
+## CLI Inference
+
+The cli command `f5-tts_infer-cli` equals to `python src/f5_tts/infer/infer_cli.py`, which is a command line tool for inference.
+
+The script will load model checkpoints from Huggingface. You can also manually download files and use `--ckpt_file` to specify the model you want to load, or directly update in `infer_cli.py`.
+
+For change vocab.txt use `--vocab_file` to provide your `vocab.txt` file.
+
+Basically you can inference with flags:
+```bash
+# Leave --ref_text "" will have ASR model transcribe (extra GPU memory usage)
+f5-tts_infer-cli \
+--model "F5-TTS" \
+--ref_audio "ref_audio.wav" \
+--ref_text "The content, subtitle or transcription of reference audio." \
+--gen_text "Some text you want TTS model generate for you."
+```
+
+And a `.toml` file would help with more flexible usage.
+
+```bash
+f5-tts_infer-cli -c custom.toml
+```
+
+For example, you can use `.toml` to pass in variables, refer to `src/f5_tts/infer/examples/basic/basic.toml`:
+
+```toml
+# F5-TTS | E2-TTS
+model = "F5-TTS"
+ref_audio = "infer/examples/basic/basic_ref_en.wav"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = "Some call me nature, others call me mother nature."
+gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring."
+# File with text to generate. Ignores the text above.
+gen_file = ""
+remove_silence = false
+output_dir = "tests"
+```
+
+You can also leverage `.toml` file to do multi-style generation, refer to `src/f5_tts/infer/examples/multi/story.toml`.
+
+```toml
+# F5-TTS | E2-TTS
+model = "F5-TTS"
+ref_audio = "infer/examples/multi/main.flac"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = ""
+gen_text = ""
+# File with text to generate. Ignores the text above.
+gen_file = "infer/examples/multi/story.txt"
+remove_silence = true
+output_dir = "tests"
+
+[voices.town]
+ref_audio = "infer/examples/multi/town.flac"
+ref_text = ""
+
+[voices.country]
+ref_audio = "infer/examples/multi/country.flac"
+ref_text = ""
+```
+You should mark the voice with `[main]` `[town]` `[country]` whenever you want to change voice, refer to `src/f5_tts/infer/examples/multi/story.txt`.
+
+## Speech Editing
+
+To test speech editing capabilities, use the following command:
+
+```bash
+python src/f5_tts/infer/speech_edit.py
+```

src/f5_tts/infer/examples/basic/basic.toml

@@ -0,0 +1,10 @@
+# F5-TTS | E2-TTS
+model = "F5-TTS"
+ref_audio = "infer/examples/basic/basic_ref_en.wav"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = "Some call me nature, others call me mother nature."
+gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring."
+# File with text to generate. Ignores the text above.
+gen_file = ""
+remove_silence = false
+output_dir = "tests"

src/f5_tts/infer/examples/multi/story.toml

@@ -0,0 +1,19 @@
+# F5-TTS | E2-TTS
+model = "F5-TTS"
+ref_audio = "infer/examples/multi/main.flac"
+# If an empty "", transcribes the reference audio automatically.
+ref_text = ""
+gen_text = ""
+# File with text to generate. Ignores the text above.
+gen_file = "infer/examples/multi/story.txt"
+remove_silence = true
+output_dir = "tests"
+
+[voices.town]
+ref_audio = "infer/examples/multi/town.flac"
+ref_text = ""
+
+[voices.country]
+ref_audio = "infer/examples/multi/country.flac"
+ref_text = ""
+

src/f5_tts/infer/examples/multi/story.txt

@@ -0,0 +1 @@
+A Town Mouse and a Country Mouse were acquaintances, and the Country Mouse one day invited his friend to come and see him at his home in the fields. The Town Mouse came, and they sat down to a dinner of barleycorns and roots, the latter of which had a distinctly earthy flavour. The fare was not much to the taste of the guest, and presently he broke out with [town] “My poor dear friend, you live here no better than the ants. Now, you should just see how I fare! My larder is a regular horn of plenty. You must come and stay with me, and I promise you you shall live on the fat of the land.” [main] So when he returned to town he took the Country Mouse with him, and showed him into a larder containing flour and oatmeal and figs and honey and dates. The Country Mouse had never seen anything like it, and sat down to enjoy the luxuries his friend provided: but before they had well begun, the door of the larder opened and someone came in. The two Mice scampered off and hid themselves in a narrow and exceedingly uncomfortable hole. Presently, when all was quiet, they ventured out again; but someone else came in, and off they scuttled again. This was too much for the visitor. [country] “Goodbye,” [main] said he, [country] “I’m off. You live in the lap of luxury, I can see, but you are surrounded by dangers; whereas at home I can enjoy my simple dinner of roots and corn in peace.”

src/f5_tts/infer/examples/vocab.txt

@@ -0,0 +1,2545 @@
+ 
+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+:
+;
+=
+>
+?
+@
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+[
+\
+]
+_
+a
+a1
+ai1
+ai2
+ai3
+ai4
+an1
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+an4
+ang1
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+내
+냐
+냥
+너
+넘
+넣
+네
+녁
+년
+녕
+노
+녹
+놀
+누
+눈
+느
+는
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+니
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+닙
+다
+닥
+단
+달
+닭
+당
+대
+더
+덕
+던
+덥
+데
+도
+독
+동
+돼
+됐
+되
+된
+될
+두
+둑
+둥
+드
+들
+등
+디
+따
+딱
+딸
+땅
+때
+떤
+떨
+떻
+또
+똑
+뚱
+뛰
+뜻
+띠
+라
+락
+란
+람
+랍
+랑
+래
+랜
+러
+런
+럼
+렇
+레
+려
+력
+렵
+렸
+로
+록
+롬
+루
+르
+른
+를
+름
+릉
+리
+릴
+림
+마
+막
+만
+많
+말
+맑
+맙
+맛
+매
+머
+먹
+멍
+메
+면
+명
+몇
+모
+목
+몸
+못
+무
+문
+물
+뭐
+뭘
+미
+민
+밌
+밑
+바
+박
+밖
+반
+받
+발
+밤
+밥
+방
+배
+백
+밸
+뱀
+버
+번
+벌
+벚
+베
+벼
+벽
+별
+병
+보
+복
+본
+볼
+봐
+봤
+부
+분
+불
+비
+빔
+빛
+빠
+빨
+뼈
+뽀
+뿅
+쁘
+사
+산
+살
+삼
+샀
+상
+새
+색
+생
+서
+선
+설
+섭
+섰
+성
+세
+셔
+션
+셨
+소
+속
+손
+송
+수
+숙
+순
+술
+숫
+숭
+숲
+쉬
+쉽
+스
+슨
+습
+슷
+시
+식
+신
+실
+싫
+심
+십
+싶
+싸
+써
+쓰
+쓴
+씌
+씨
+씩
+씬
+아
+악
+안
+않
+알
+야
+약
+얀
+양
+얘
+어
+언
+얼
+엄
+업
+없
+었
+엉
+에
+여
+역
+연
+염
+엽
+영
+옆
+예
+옛
+오
+온
+올
+옷
+옹
+와
+왔
+왜
+요
+욕
+용
+우
+운
+울
+웃
+워
+원
+월
+웠
+위
+윙
+유
+육
+윤
+으
+은
+을
+음
+응
+의
+이
+익
+인
+일
+읽
+임
+입
+있
+자
+작
+잔
+잖
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+잡
+잤
+장
+재
+저
+전
+점
+정
+제
+져
+졌
+조
+족
+좀
+종
+좋
+죠
+주
+준
+줄
+중
+줘
+즈
+즐
+즘
+지
+진
+집
+짜
+짝
+쩌
+쪼
+쪽
+쫌
+쭈
+쯔
+찌
+찍
+차
+착
+찾
+책
+처
+천
+철
+체
+쳐
+쳤
+초
+촌
+추
+출
+춤
+춥
+춰
+치
+친
+칠
+침
+칩
+칼
+커
+켓
+코
+콩
+쿠
+퀴
+크
+큰
+큽
+키
+킨
+타
+태
+터
+턴
+털
+테
+토
+통
+투
+트
+특
+튼
+틀
+티
+팀
+파
+팔
+패
+페
+펜
+펭
+평
+포
+폭
+표
+품
+풍
+프
+플
+피
+필
+하
+학
+한
+할
+함
+합
+항
+해
+햇
+했
+행
+허
+험
+형
+혜
+호
+혼
+홀
+화
+회
+획
+후
+휴
+흐
+흔
+희
+히
+힘
+ﷺ
+ﷻ
+！
+，
+？
+�
+𠮶

src/f5_tts/infer/infer_cli.py

@@ -0,0 +1,193 @@
+import argparse
+import codecs
+import os
+import re
+from pathlib import Path
+from importlib.resources import files
+
+import numpy as np
+import soundfile as sf
+import tomli
+from cached_path import cached_path
+
+from f5_tts.model import DiT, UNetT
+from f5_tts.infer.utils_infer import (
+    load_vocoder,
+    load_model,
+    preprocess_ref_audio_text,
+    infer_process,
+    remove_silence_for_generated_wav,
+)
+
+
+parser = argparse.ArgumentParser(
+    prog="python3 infer-cli.py",
+    description="Commandline interface for E2/F5 TTS with Advanced Batch Processing.",
+    epilog="Specify options above to override one or more settings from config.",
+)
+parser.add_argument(
+    "-c",
+    "--config",
+    help="Configuration file. Default=infer/examples/basic/basic.toml",
+    default=os.path.join(files("f5_tts").joinpath("infer/examples/basic"), "basic.toml"),
+)
+parser.add_argument(
+    "-m",
+    "--model",
+    help="F5-TTS | E2-TTS",
+)
+parser.add_argument(
+    "-p",
+    "--ckpt_file",
+    help="The Checkpoint .pt",
+)
+parser.add_argument(
+    "-v",
+    "--vocab_file",
+    help="The vocab .txt",
+)
+parser.add_argument("-r", "--ref_audio", type=str, help="Reference audio file < 15 seconds.")
+parser.add_argument("-s", "--ref_text", type=str, default="666", help="Subtitle for the reference audio.")
+parser.add_argument(
+    "-t",
+    "--gen_text",
+    type=str,
+    help="Text to generate.",
+)
+parser.add_argument(
+    "-f",
+    "--gen_file",
+    type=str,
+    help="File with text to generate. Ignores --text",
+)
+parser.add_argument(
+    "-o",
+    "--output_dir",
+    type=str,
+    help="Path to output folder..",
+)
+parser.add_argument(
+    "--remove_silence",
+    help="Remove silence.",
+)
+parser.add_argument(
+    "--load_vocoder_from_local",
+    action="store_true",
+    help="load vocoder from local. Default: ../checkpoints/charactr/vocos-mel-24khz",
+)
+args = parser.parse_args()
+
+config = tomli.load(open(args.config, "rb"))
+
+ref_audio = args.ref_audio if args.ref_audio else config["ref_audio"]
+ref_text = args.ref_text if args.ref_text != "666" else config["ref_text"]
+gen_text = args.gen_text if args.gen_text else config["gen_text"]
+gen_file = args.gen_file if args.gen_file else config["gen_file"]
+
+# patches for pip pkg user
+if "infer/examples/" in ref_audio:
+    ref_audio = str(files("f5_tts").joinpath(f"{ref_audio}"))
+if "infer/examples/" in gen_file:
+    gen_file = str(files("f5_tts").joinpath(f"{gen_file}"))
+if "voices" in config:
+    for voice in config["voices"]:
+        voice_ref_audio = config["voices"][voice]["ref_audio"]
+        if "infer/examples/" in voice_ref_audio:
+            config["voices"][voice]["ref_audio"] = str(files("f5_tts").joinpath(f"{voice_ref_audio}"))
+
+if gen_file:
+    gen_text = codecs.open(gen_file, "r", "utf-8").read()
+output_dir = args.output_dir if args.output_dir else config["output_dir"]
+model = args.model if args.model else config["model"]
+ckpt_file = args.ckpt_file if args.ckpt_file else ""
+vocab_file = args.vocab_file if args.vocab_file else ""
+remove_silence = args.remove_silence if args.remove_silence else config["remove_silence"]
+wave_path = Path(output_dir) / "infer_cli_out.wav"
+# spectrogram_path = Path(output_dir) / "infer_cli_out.png"
+vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
+
+vocos = load_vocoder(is_local=args.load_vocoder_from_local, local_path=vocos_local_path)
+
+
+# load models
+if model == "F5-TTS":
+    model_cls = DiT
+    model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+    if ckpt_file == "":
+        repo_name = "F5-TTS"
+        exp_name = "F5TTS_Base"
+        ckpt_step = 1200000
+        ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
+        # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors; local path
+
+elif model == "E2-TTS":
+    model_cls = UNetT
+    model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+    if ckpt_file == "":
+        repo_name = "E2-TTS"
+        exp_name = "E2TTS_Base"
+        ckpt_step = 1200000
+        ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors"))
+        # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt"  # .pt | .safetensors; local path
+
+print(f"Using {model}...")
+ema_model = load_model(model_cls, model_cfg, ckpt_file, vocab_file)
+
+
+def main_process(ref_audio, ref_text, text_gen, model_obj, remove_silence):
+    main_voice = {"ref_audio": ref_audio, "ref_text": ref_text}
+    if "voices" not in config:
+        voices = {"main": main_voice}
+    else:
+        voices = config["voices"]
+        voices["main"] = main_voice
+    for voice in voices:
+        voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text(
+            voices[voice]["ref_audio"], voices[voice]["ref_text"]
+        )
+        print("Voice:", voice)
+        print("Ref_audio:", voices[voice]["ref_audio"])
+        print("Ref_text:", voices[voice]["ref_text"])
+
+    generated_audio_segments = []
+    reg1 = r"(?=\[\w+\])"
+    chunks = re.split(reg1, text_gen)
+    reg2 = r"\[(\w+)\]"
+    for text in chunks:
+        match = re.match(reg2, text)
+        if match:
+            voice = match[1]
+        else:
+            print("No voice tag found, using main.")
+            voice = "main"
+        if voice not in voices:
+            print(f"Voice {voice} not found, using main.")
+            voice = "main"
+        text = re.sub(reg2, "", text)
+        gen_text = text.strip()
+        ref_audio = voices[voice]["ref_audio"]
+        ref_text = voices[voice]["ref_text"]
+        print(f"Voice: {voice}")
+        audio, final_sample_rate, spectragram = infer_process(ref_audio, ref_text, gen_text, model_obj)
+        generated_audio_segments.append(audio)
+
+    if generated_audio_segments:
+        final_wave = np.concatenate(generated_audio_segments)
+
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+
+        with open(wave_path, "wb") as f:
+            sf.write(f.name, final_wave, final_sample_rate)
+            # Remove silence
+            if remove_silence:
+                remove_silence_for_generated_wav(f.name)
+            print(f.name)
+
+
+def main():
+    main_process(ref_audio, ref_text, gen_text, ema_model, remove_silence)
+
+
+if __name__ == "__main__":
+    main()

src/f5_tts/infer/speech_edit.py

@@ -0,0 +1,191 @@
+import os
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+from vocos import Vocos
+
+from f5_tts.model import CFM, UNetT, DiT
+from f5_tts.model.utils import (
+    get_tokenizer,
+    convert_char_to_pinyin,
+)
+from f5_tts.infer.utils_infer import (
+    load_checkpoint,
+    save_spectrogram,
+)
+
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+
+# --------------------- Dataset Settings -------------------- #
+
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+target_rms = 0.1
+
+tokenizer = "pinyin"
+dataset_name = "Emilia_ZH_EN"
+
+
+# ---------------------- infer setting ---------------------- #
+
+seed = None  # int | None
+
+exp_name = "F5TTS_Base"  # F5TTS_Base | E2TTS_Base
+ckpt_step = 1200000
+
+nfe_step = 32  # 16, 32
+cfg_strength = 2.0
+ode_method = "euler"  # euler | midpoint
+sway_sampling_coef = -1.0
+speed = 1.0
+
+if exp_name == "F5TTS_Base":
+    model_cls = DiT
+    model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+
+elif exp_name == "E2TTS_Base":
+    model_cls = UNetT
+    model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+
+ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.safetensors"
+output_dir = "tests"
+
+# [leverage https://github.com/MahmoudAshraf97/ctc-forced-aligner to get char level alignment]
+# pip install git+https://github.com/MahmoudAshraf97/ctc-forced-aligner.git
+# [write the origin_text into a file, e.g. tests/test_edit.txt]
+# ctc-forced-aligner --audio_path "src/f5_tts/infer/examples/basic/basic_ref_en.wav" --text_path "tests/test_edit.txt" --language "zho" --romanize --split_size "char"
+# [result will be saved at same path of audio file]
+# [--language "zho" for Chinese, "eng" for English]
+# [if local ckpt, set --alignment_model "../checkpoints/mms-300m-1130-forced-aligner"]
+
+audio_to_edit = "src/f5_tts/infer/examples/basic/basic_ref_en.wav"
+origin_text = "Some call me nature, others call me mother nature."
+target_text = "Some call me optimist, others call me realist."
+parts_to_edit = [
+    [1.42, 2.44],
+    [4.04, 4.9],
+]  # stard_ends of "nature" & "mother nature", in seconds
+fix_duration = [
+    1.2,
+    1,
+]  # fix duration for "optimist" & "realist", in seconds
+
+# audio_to_edit = "src/f5_tts/infer/examples/basic/basic_ref_zh.wav"
+# origin_text = "对，这就是我，万人敬仰的太乙真人。"
+# target_text = "对，那就是你，万人敬仰的太白金星。"
+# parts_to_edit = [[0.84, 1.4], [1.92, 2.4], [4.26, 6.26], ]
+# fix_duration = None  # use origin text duration
+
+
+# -------------------------------------------------#
+
+use_ema = True
+
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+# Vocoder model
+local = False
+if local:
+    vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz"
+    vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml")
+    state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", weights_only=True, map_location=device)
+    vocos.load_state_dict(state_dict)
+
+    vocos.eval()
+else:
+    vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+
+# Tokenizer
+vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer)
+
+# Model
+model = CFM(
+    transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+    mel_spec_kwargs=dict(
+        target_sample_rate=target_sample_rate,
+        n_mel_channels=n_mel_channels,
+        hop_length=hop_length,
+    ),
+    odeint_kwargs=dict(
+        method=ode_method,
+    ),
+    vocab_char_map=vocab_char_map,
+).to(device)
+
+model = load_checkpoint(model, ckpt_path, device, use_ema=use_ema)
+
+# Audio
+audio, sr = torchaudio.load(audio_to_edit)
+if audio.shape[0] > 1:
+    audio = torch.mean(audio, dim=0, keepdim=True)
+rms = torch.sqrt(torch.mean(torch.square(audio)))
+if rms < target_rms:
+    audio = audio * target_rms / rms
+if sr != target_sample_rate:
+    resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
+    audio = resampler(audio)
+offset = 0
+audio_ = torch.zeros(1, 0)
+edit_mask = torch.zeros(1, 0, dtype=torch.bool)
+for part in parts_to_edit:
+    start, end = part
+    part_dur = end - start if fix_duration is None else fix_duration.pop(0)
+    part_dur = part_dur * target_sample_rate
+    start = start * target_sample_rate
+    audio_ = torch.cat((audio_, audio[:, round(offset) : round(start)], torch.zeros(1, round(part_dur))), dim=-1)
+    edit_mask = torch.cat(
+        (
+            edit_mask,
+            torch.ones(1, round((start - offset) / hop_length), dtype=torch.bool),
+            torch.zeros(1, round(part_dur / hop_length), dtype=torch.bool),
+        ),
+        dim=-1,
+    )
+    offset = end * target_sample_rate
+# audio = torch.cat((audio_, audio[:, round(offset):]), dim = -1)
+edit_mask = F.pad(edit_mask, (0, audio.shape[-1] // hop_length - edit_mask.shape[-1] + 1), value=True)
+audio = audio.to(device)
+edit_mask = edit_mask.to(device)
+
+# Text
+text_list = [target_text]
+if tokenizer == "pinyin":
+    final_text_list = convert_char_to_pinyin(text_list)
+else:
+    final_text_list = [text_list]
+print(f"text  : {text_list}")
+print(f"pinyin: {final_text_list}")
+
+# Duration
+ref_audio_len = 0
+duration = audio.shape[-1] // hop_length
+
+# Inference
+with torch.inference_mode():
+    generated, trajectory = model.sample(
+        cond=audio,
+        text=final_text_list,
+        duration=duration,
+        steps=nfe_step,
+        cfg_strength=cfg_strength,
+        sway_sampling_coef=sway_sampling_coef,
+        seed=seed,
+        edit_mask=edit_mask,
+    )
+print(f"Generated mel: {generated.shape}")
+
+# Final result
+generated = generated.to(torch.float32)
+generated = generated[:, ref_audio_len:, :]
+generated_mel_spec = generated.permute(0, 2, 1)
+generated_wave = vocos.decode(generated_mel_spec.cpu())
+if rms < target_rms:
+    generated_wave = generated_wave * rms / target_rms
+
+save_spectrogram(generated_mel_spec[0].cpu().numpy(), f"{output_dir}/speech_edit_out.png")
+torchaudio.save(f"{output_dir}/speech_edit_out.wav", generated_wave, target_sample_rate)
+print(f"Generated wav: {generated_wave.shape}")

src/f5_tts/infer/utils_infer.py

@@ -0,0 +1,417 @@
+# A unified script for inference process
+# Make adjustments inside functions, and consider both gradio and cli scripts if need to change func output format
+
+import hashlib
+import re
+import tempfile
+from importlib.resources import files
+
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pylab as plt
+import numpy as np
+import torch
+import torchaudio
+import tqdm
+from pydub import AudioSegment, silence
+from transformers import pipeline
+from vocos import Vocos
+
+from f5_tts.model import CFM
+from f5_tts.model.utils import (
+    get_tokenizer,
+    convert_char_to_pinyin,
+)
+
+_ref_audio_cache = {}
+
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+
+
+# -----------------------------------------
+
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+target_rms = 0.1
+cross_fade_duration = 0.15
+ode_method = "euler"
+nfe_step = 32  # 16, 32
+cfg_strength = 2.0
+sway_sampling_coef = -1.0
+speed = 1.0
+fix_duration = None
+
+# -----------------------------------------
+
+
+# chunk text into smaller pieces
+
+
+def chunk_text(text, max_chars=135):
+    """
+    Splits the input text into chunks, each with a maximum number of characters.
+
+    Args:
+        text (str): The text to be split.
+        max_chars (int): The maximum number of characters per chunk.
+
+    Returns:
+        List[str]: A list of text chunks.
+    """
+    chunks = []
+    current_chunk = ""
+    # Split the text into sentences based on punctuation followed by whitespace
+    sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[；：，。！？])", text)
+
+    for sentence in sentences:
+        if len(current_chunk.encode("utf-8")) + len(sentence.encode("utf-8")) <= max_chars:
+            current_chunk += sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence
+        else:
+            if current_chunk:
+                chunks.append(current_chunk.strip())
+            current_chunk = sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence
+
+    if current_chunk:
+        chunks.append(current_chunk.strip())
+
+    return chunks
+
+
+# load vocoder
+def load_vocoder(is_local=False, local_path="", device=device):
+    if is_local:
+        print(f"Load vocos from local path {local_path}")
+        vocos = Vocos.from_hparams(f"{local_path}/config.yaml")
+        state_dict = torch.load(f"{local_path}/pytorch_model.bin", map_location=device)
+        vocos.load_state_dict(state_dict)
+        vocos.eval()
+    else:
+        print("Download Vocos from huggingface charactr/vocos-mel-24khz")
+        vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+    return vocos
+
+
+# load asr pipeline
+
+asr_pipe = None
+
+
+def initialize_asr_pipeline(device=device):
+    global asr_pipe
+    asr_pipe = pipeline(
+        "automatic-speech-recognition",
+        model="openai/whisper-large-v3-turbo",
+        torch_dtype=torch.float16,
+        device=device,
+    )
+
+
+# load model checkpoint for inference
+
+
+def load_checkpoint(model, ckpt_path, device, use_ema=True):
+    if device == "cuda":
+        model = model.half()
+
+    ckpt_type = ckpt_path.split(".")[-1]
+    if ckpt_type == "safetensors":
+        from safetensors.torch import load_file
+
+        checkpoint = load_file(ckpt_path)
+    else:
+        checkpoint = torch.load(ckpt_path, weights_only=True)
+
+    if use_ema:
+        if ckpt_type == "safetensors":
+            checkpoint = {"ema_model_state_dict": checkpoint}
+        checkpoint["model_state_dict"] = {
+            k.replace("ema_model.", ""): v
+            for k, v in checkpoint["ema_model_state_dict"].items()
+            if k not in ["initted", "step"]
+        }
+        model.load_state_dict(checkpoint["model_state_dict"])
+    else:
+        if ckpt_type == "safetensors":
+            checkpoint = {"model_state_dict": checkpoint}
+        model.load_state_dict(checkpoint["model_state_dict"])
+
+    return model.to(device)
+
+
+# load model for inference
+
+
+def load_model(model_cls, model_cfg, ckpt_path, vocab_file="", ode_method=ode_method, use_ema=True, device=device):
+    if vocab_file == "":
+        vocab_file = str(files("f5_tts").joinpath("infer/examples/vocab.txt"))
+    tokenizer = "custom"
+
+    print("\nvocab : ", vocab_file)
+    print("tokenizer : ", tokenizer)
+    print("model : ", ckpt_path, "\n")
+
+    vocab_char_map, vocab_size = get_tokenizer(vocab_file, tokenizer)
+    model = CFM(
+        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=dict(
+            target_sample_rate=target_sample_rate,
+            n_mel_channels=n_mel_channels,
+            hop_length=hop_length,
+        ),
+        odeint_kwargs=dict(
+            method=ode_method,
+        ),
+        vocab_char_map=vocab_char_map,
+    ).to(device)
+
+    model = load_checkpoint(model, ckpt_path, device, use_ema=use_ema)
+
+    return model
+
+
+# preprocess reference audio and text
+
+
+def preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=print, device=device):
+    show_info("Converting audio...")
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+        aseg = AudioSegment.from_file(ref_audio_orig)
+
+        non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=1000)
+        non_silent_wave = AudioSegment.silent(duration=0)
+        for non_silent_seg in non_silent_segs:
+            if len(non_silent_wave) > 10000 and len(non_silent_wave + non_silent_seg) > 18000:
+                show_info("Audio is over 18s, clipping short.")
+                break
+            non_silent_wave += non_silent_seg
+        aseg = non_silent_wave
+
+        aseg.export(f.name, format="wav")
+        ref_audio = f.name
+
+    # Compute a hash of the reference audio file
+    with open(ref_audio, "rb") as audio_file:
+        audio_data = audio_file.read()
+        audio_hash = hashlib.md5(audio_data).hexdigest()
+
+    global _ref_audio_cache
+    if audio_hash in _ref_audio_cache:
+        # Use cached reference text
+        show_info("Using cached reference text...")
+        ref_text = _ref_audio_cache[audio_hash]
+    else:
+        if not ref_text.strip():
+            global asr_pipe
+            if asr_pipe is None:
+                initialize_asr_pipeline(device=device)
+            show_info("No reference text provided, transcribing reference audio...")
+            ref_text = asr_pipe(
+                ref_audio,
+                chunk_length_s=30,
+                batch_size=128,
+                generate_kwargs={"task": "transcribe"},
+                return_timestamps=False,
+            )["text"].strip()
+            show_info("Finished transcription")
+        else:
+            show_info("Using custom reference text...")
+        # Cache the transcribed text
+        _ref_audio_cache[audio_hash] = ref_text
+
+    # Ensure ref_text ends with a proper sentence-ending punctuation
+    if not ref_text.endswith(". ") and not ref_text.endswith("。"):
+        if ref_text.endswith("."):
+            ref_text += " "
+        else:
+            ref_text += ". "
+
+    return ref_audio, ref_text
+
+
+# infer process: chunk text -> infer batches [i.e. infer_batch_process()]
+
+
+def infer_process(
+    ref_audio,
+    ref_text,
+    gen_text,
+    model_obj,
+    show_info=print,
+    progress=tqdm,
+    target_rms=target_rms,
+    cross_fade_duration=cross_fade_duration,
+    nfe_step=nfe_step,
+    cfg_strength=cfg_strength,
+    sway_sampling_coef=sway_sampling_coef,
+    speed=speed,
+    fix_duration=fix_duration,
+    device=device,
+):
+    # Split the input text into batches
+    audio, sr = torchaudio.load(ref_audio)
+    max_chars = int(len(ref_text.encode("utf-8")) / (audio.shape[-1] / sr) * (25 - audio.shape[-1] / sr))
+    gen_text_batches = chunk_text(gen_text, max_chars=max_chars)
+    for i, gen_text in enumerate(gen_text_batches):
+        print(f"gen_text {i}", gen_text)
+
+    show_info(f"Generating audio in {len(gen_text_batches)} batches...")
+    return infer_batch_process(
+        (audio, sr),
+        ref_text,
+        gen_text_batches,
+        model_obj,
+        progress=progress,
+        target_rms=target_rms,
+        cross_fade_duration=cross_fade_duration,
+        nfe_step=nfe_step,
+        cfg_strength=cfg_strength,
+        sway_sampling_coef=sway_sampling_coef,
+        speed=speed,
+        fix_duration=fix_duration,
+        device=device,
+    )
+
+
+# infer batches
+
+
+def infer_batch_process(
+    ref_audio,
+    ref_text,
+    gen_text_batches,
+    model_obj,
+    progress=tqdm,
+    target_rms=0.1,
+    cross_fade_duration=0.15,
+    nfe_step=32,
+    cfg_strength=2.0,
+    sway_sampling_coef=-1,
+    speed=1,
+    fix_duration=None,
+    device=None,
+):
+    audio, sr = ref_audio
+    if audio.shape[0] > 1:
+        audio = torch.mean(audio, dim=0, keepdim=True)
+
+    rms = torch.sqrt(torch.mean(torch.square(audio)))
+    if rms < target_rms:
+        audio = audio * target_rms / rms
+    if sr != target_sample_rate:
+        resampler = torchaudio.transforms.Resample(sr, target_sample_rate)
+        audio = resampler(audio)
+    audio = audio.to(device)
+
+    generated_waves = []
+    spectrograms = []
+
+    if len(ref_text[-1].encode("utf-8")) == 1:
+        ref_text = ref_text + " "
+    for i, gen_text in enumerate(progress.tqdm(gen_text_batches)):
+        # Prepare the text
+        text_list = [ref_text + gen_text]
+        final_text_list = convert_char_to_pinyin(text_list)
+
+        ref_audio_len = audio.shape[-1] // hop_length
+        if fix_duration is not None:
+            duration = int(fix_duration * target_sample_rate / hop_length)
+        else:
+            # Calculate duration
+            ref_text_len = len(ref_text.encode("utf-8"))
+            gen_text_len = len(gen_text.encode("utf-8"))
+            duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed)
+
+        # inference
+        with torch.inference_mode():
+            generated, _ = model_obj.sample(
+                cond=audio,
+                text=final_text_list,
+                duration=duration,
+                steps=nfe_step,
+                cfg_strength=cfg_strength,
+                sway_sampling_coef=sway_sampling_coef,
+            )
+
+        generated = generated.to(torch.float32)
+        generated = generated[:, ref_audio_len:, :]
+        generated_mel_spec = generated.permute(0, 2, 1)
+        generated_wave = vocos.decode(generated_mel_spec.cpu())
+        if rms < target_rms:
+            generated_wave = generated_wave * rms / target_rms
+
+        # wav -> numpy
+        generated_wave = generated_wave.squeeze().cpu().numpy()
+
+        generated_waves.append(generated_wave)
+        spectrograms.append(generated_mel_spec[0].cpu().numpy())
+
+    # Combine all generated waves with cross-fading
+    if cross_fade_duration <= 0:
+        # Simply concatenate
+        final_wave = np.concatenate(generated_waves)
+    else:
+        final_wave = generated_waves[0]
+        for i in range(1, len(generated_waves)):
+            prev_wave = final_wave
+            next_wave = generated_waves[i]
+
+            # Calculate cross-fade samples, ensuring it does not exceed wave lengths
+            cross_fade_samples = int(cross_fade_duration * target_sample_rate)
+            cross_fade_samples = min(cross_fade_samples, len(prev_wave), len(next_wave))
+
+            if cross_fade_samples <= 0:
+                # No overlap possible, concatenate
+                final_wave = np.concatenate([prev_wave, next_wave])
+                continue
+
+            # Overlapping parts
+            prev_overlap = prev_wave[-cross_fade_samples:]
+            next_overlap = next_wave[:cross_fade_samples]
+
+            # Fade out and fade in
+            fade_out = np.linspace(1, 0, cross_fade_samples)
+            fade_in = np.linspace(0, 1, cross_fade_samples)
+
+            # Cross-faded overlap
+            cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in
+
+            # Combine
+            new_wave = np.concatenate(
+                [prev_wave[:-cross_fade_samples], cross_faded_overlap, next_wave[cross_fade_samples:]]
+            )
+
+            final_wave = new_wave
+
+    # Create a combined spectrogram
+    combined_spectrogram = np.concatenate(spectrograms, axis=1)
+
+    return final_wave, target_sample_rate, combined_spectrogram
+
+
+# remove silence from generated wav
+
+
+def remove_silence_for_generated_wav(filename):
+    aseg = AudioSegment.from_file(filename)
+    non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500)
+    non_silent_wave = AudioSegment.silent(duration=0)
+    for non_silent_seg in non_silent_segs:
+        non_silent_wave += non_silent_seg
+    aseg = non_silent_wave
+    aseg.export(filename, format="wav")
+
+
+# save spectrogram
+
+
+def save_spectrogram(spectrogram, path):
+    plt.figure(figsize=(12, 4))
+    plt.imshow(spectrogram, origin="lower", aspect="auto")
+    plt.colorbar()
+    plt.savefig(path)
+    plt.close()

src/f5_tts/model/__init__.py

@@ -0,0 +1,10 @@
+from f5_tts.model.cfm import CFM
+
+from f5_tts.model.backbones.unett import UNetT
+from f5_tts.model.backbones.dit import DiT
+from f5_tts.model.backbones.mmdit import MMDiT
+
+from f5_tts.model.trainer import Trainer
+
+
+__all__ = ["CFM", "UNetT", "DiT", "MMDiT", "Trainer"]

src/f5_tts/model/backbones/README.md

@@ -0,0 +1,20 @@
+## Backbones quick introduction
+
+
+### unett.py
+- flat unet transformer
+- structure same as in e2-tts & voicebox paper except using rotary pos emb
+- update: allow possible abs pos emb & convnextv2 blocks for embedded text before concat
+
+### dit.py
+- adaln-zero dit
+- embedded timestep as condition
+- concatted noised_input + masked_cond + embedded_text, linear proj in
+- possible abs pos emb & convnextv2 blocks for embedded text before concat
+- possible long skip connection (first layer to last layer)
+
+### mmdit.py
+- sd3 structure
+- timestep as condition
+- left stream: text embedded and applied a abs pos emb
+- right stream: masked_cond & noised_input concatted and with same conv pos emb as unett

src/f5_tts/model/backbones/dit.py

@@ -0,0 +1,163 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from x_transformers.x_transformers import RotaryEmbedding
+
+from f5_tts.model.modules import (
+    TimestepEmbedding,
+    ConvNeXtV2Block,
+    ConvPositionEmbedding,
+    DiTBlock,
+    AdaLayerNormZero_Final,
+    precompute_freqs_cis,
+    get_pos_embed_indices,
+)
+
+
+# Text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+
+        if conv_layers > 0:
+            self.extra_modeling = True
+            self.precompute_max_pos = 4096  # ~44s of 24khz audio
+            self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
+            self.text_blocks = nn.Sequential(
+                *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
+            )
+        else:
+            self.extra_modeling = False
+
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):  # noqa: F722
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
+        batch, text_len = text.shape[0], text.shape[1]
+        text = F.pad(text, (0, seq_len - text_len), value=0)
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b n -> b n d
+
+        # possible extra modeling
+        if self.extra_modeling:
+            # sinus pos emb
+            batch_start = torch.zeros((batch,), dtype=torch.long)
+            pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
+            text_pos_embed = self.freqs_cis[pos_idx]
+            text = text + text_pos_embed
+
+            # convnextv2 blocks
+            text = self.text_blocks(text)
+
+        return text
+
+
+# noised input audio and context mixing embedding
+
+
+class InputEmbedding(nn.Module):
+    def __init__(self, mel_dim, text_dim, out_dim):
+        super().__init__()
+        self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:  # cfg for cond audio
+            cond = torch.zeros_like(cond)
+
+        x = self.proj(torch.cat((x, cond, text_embed), dim=-1))
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Transformer backbone using DiT blocks
+
+
+class DiT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=100,
+        text_num_embeds=256,
+        text_dim=None,
+        conv_layers=0,
+        long_skip_connection=False,
+    ):
+        super().__init__()
+
+        self.time_embed = TimestepEmbedding(dim)
+        if text_dim is None:
+            text_dim = mel_dim
+        self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers)
+        self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        self.dim = dim
+        self.depth = depth
+
+        self.transformer_blocks = nn.ModuleList(
+            [DiTBlock(dim=dim, heads=heads, dim_head=dim_head, ff_mult=ff_mult, dropout=dropout) for _ in range(depth)]
+        )
+        self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None
+
+        self.norm_out = AdaLayerNormZero_Final(dim)  # final modulation
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        drop_audio_cond,  # cfg for cond audio
+        drop_text,  # cfg for text
+        mask: bool["b n"] | None = None,  # noqa: F722
+    ):
+        batch, seq_len = x.shape[0], x.shape[1]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(time)
+        text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+        x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
+
+        rope = self.rotary_embed.forward_from_seq_len(seq_len)
+
+        if self.long_skip_connection is not None:
+            residual = x
+
+        for block in self.transformer_blocks:
+            x = block(x, t, mask=mask, rope=rope)
+
+        if self.long_skip_connection is not None:
+            x = self.long_skip_connection(torch.cat((x, residual), dim=-1))
+
+        x = self.norm_out(x, t)
+        output = self.proj_out(x)
+
+        return output

src/f5_tts/model/backbones/mmdit.py

@@ -0,0 +1,146 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+
+from x_transformers.x_transformers import RotaryEmbedding
+
+from f5_tts.model.modules import (
+    TimestepEmbedding,
+    ConvPositionEmbedding,
+    MMDiTBlock,
+    AdaLayerNormZero_Final,
+    precompute_freqs_cis,
+    get_pos_embed_indices,
+)
+
+
+# text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, out_dim, text_num_embeds):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, out_dim)  # will use 0 as filler token
+
+        self.precompute_max_pos = 1024
+        self.register_buffer("freqs_cis", precompute_freqs_cis(out_dim, self.precompute_max_pos), persistent=False)
+
+    def forward(self, text: int["b nt"], drop_text=False) -> int["b nt d"]:  # noqa: F722
+        text = text + 1
+        if drop_text:
+            text = torch.zeros_like(text)
+        text = self.text_embed(text)
+
+        # sinus pos emb
+        batch_start = torch.zeros((text.shape[0],), dtype=torch.long)
+        batch_text_len = text.shape[1]
+        pos_idx = get_pos_embed_indices(batch_start, batch_text_len, max_pos=self.precompute_max_pos)
+        text_pos_embed = self.freqs_cis[pos_idx]
+
+        text = text + text_pos_embed
+
+        return text
+
+
+# noised input & masked cond audio embedding
+
+
+class AudioEmbedding(nn.Module):
+    def __init__(self, in_dim, out_dim):
+        super().__init__()
+        self.linear = nn.Linear(2 * in_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:
+            cond = torch.zeros_like(cond)
+        x = torch.cat((x, cond), dim=-1)
+        x = self.linear(x)
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Transformer backbone using MM-DiT blocks
+
+
+class MMDiT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        text_num_embeds=256,
+        mel_dim=100,
+    ):
+        super().__init__()
+
+        self.time_embed = TimestepEmbedding(dim)
+        self.text_embed = TextEmbedding(dim, text_num_embeds)
+        self.audio_embed = AudioEmbedding(mel_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        self.dim = dim
+        self.depth = depth
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                MMDiTBlock(
+                    dim=dim,
+                    heads=heads,
+                    dim_head=dim_head,
+                    dropout=dropout,
+                    ff_mult=ff_mult,
+                    context_pre_only=i == depth - 1,
+                )
+                for i in range(depth)
+            ]
+        )
+        self.norm_out = AdaLayerNormZero_Final(dim)  # final modulation
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        drop_audio_cond,  # cfg for cond audio
+        drop_text,  # cfg for text
+        mask: bool["b n"] | None = None,  # noqa: F722
+    ):
+        batch = x.shape[0]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning (time), c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(time)
+        c = self.text_embed(text, drop_text=drop_text)
+        x = self.audio_embed(x, cond, drop_audio_cond=drop_audio_cond)
+
+        seq_len = x.shape[1]
+        text_len = text.shape[1]
+        rope_audio = self.rotary_embed.forward_from_seq_len(seq_len)
+        rope_text = self.rotary_embed.forward_from_seq_len(text_len)
+
+        for block in self.transformer_blocks:
+            c, x = block(x, c, t, mask=mask, rope=rope_audio, c_rope=rope_text)
+
+        x = self.norm_out(x, t)
+        output = self.proj_out(x)
+
+        return output

src/f5_tts/model/backbones/unett.py

@@ -0,0 +1,219 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+from typing import Literal
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from x_transformers import RMSNorm
+from x_transformers.x_transformers import RotaryEmbedding
+
+from f5_tts.model.modules import (
+    TimestepEmbedding,
+    ConvNeXtV2Block,
+    ConvPositionEmbedding,
+    Attention,
+    AttnProcessor,
+    FeedForward,
+    precompute_freqs_cis,
+    get_pos_embed_indices,
+)
+
+
+# Text embedding
+
+
+class TextEmbedding(nn.Module):
+    def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2):
+        super().__init__()
+        self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
+
+        if conv_layers > 0:
+            self.extra_modeling = True
+            self.precompute_max_pos = 4096  # ~44s of 24khz audio
+            self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
+            self.text_blocks = nn.Sequential(
+                *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
+            )
+        else:
+            self.extra_modeling = False
+
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):  # noqa: F722
+        text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
+        text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
+        batch, text_len = text.shape[0], text.shape[1]
+        text = F.pad(text, (0, seq_len - text_len), value=0)
+
+        if drop_text:  # cfg for text
+            text = torch.zeros_like(text)
+
+        text = self.text_embed(text)  # b n -> b n d
+
+        # possible extra modeling
+        if self.extra_modeling:
+            # sinus pos emb
+            batch_start = torch.zeros((batch,), dtype=torch.long)
+            pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
+            text_pos_embed = self.freqs_cis[pos_idx]
+            text = text + text_pos_embed
+
+            # convnextv2 blocks
+            text = self.text_blocks(text)
+
+        return text
+
+
+# noised input audio and context mixing embedding
+
+
+class InputEmbedding(nn.Module):
+    def __init__(self, mel_dim, text_dim, out_dim):
+        super().__init__()
+        self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim)
+        self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim)
+
+    def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], drop_audio_cond=False):  # noqa: F722
+        if drop_audio_cond:  # cfg for cond audio
+            cond = torch.zeros_like(cond)
+
+        x = self.proj(torch.cat((x, cond, text_embed), dim=-1))
+        x = self.conv_pos_embed(x) + x
+        return x
+
+
+# Flat UNet Transformer backbone
+
+
+class UNetT(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth=8,
+        heads=8,
+        dim_head=64,
+        dropout=0.1,
+        ff_mult=4,
+        mel_dim=100,
+        text_num_embeds=256,
+        text_dim=None,
+        conv_layers=0,
+        skip_connect_type: Literal["add", "concat", "none"] = "concat",
+    ):
+        super().__init__()
+        assert depth % 2 == 0, "UNet-Transformer's depth should be even."
+
+        self.time_embed = TimestepEmbedding(dim)
+        if text_dim is None:
+            text_dim = mel_dim
+        self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers)
+        self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
+
+        self.rotary_embed = RotaryEmbedding(dim_head)
+
+        # transformer layers & skip connections
+
+        self.dim = dim
+        self.skip_connect_type = skip_connect_type
+        needs_skip_proj = skip_connect_type == "concat"
+
+        self.depth = depth
+        self.layers = nn.ModuleList([])
+
+        for idx in range(depth):
+            is_later_half = idx >= (depth // 2)
+
+            attn_norm = RMSNorm(dim)
+            attn = Attention(
+                processor=AttnProcessor(),
+                dim=dim,
+                heads=heads,
+                dim_head=dim_head,
+                dropout=dropout,
+            )
+
+            ff_norm = RMSNorm(dim)
+            ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+            skip_proj = nn.Linear(dim * 2, dim, bias=False) if needs_skip_proj and is_later_half else None
+
+            self.layers.append(
+                nn.ModuleList(
+                    [
+                        skip_proj,
+                        attn_norm,
+                        attn,
+                        ff_norm,
+                        ff,
+                    ]
+                )
+            )
+
+        self.norm_out = RMSNorm(dim)
+        self.proj_out = nn.Linear(dim, mel_dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # nosied input audio  # noqa: F722
+        cond: float["b n d"],  # masked cond audio  # noqa: F722
+        text: int["b nt"],  # text  # noqa: F722
+        time: float["b"] | float[""],  # time step  # noqa: F821 F722
+        drop_audio_cond,  # cfg for cond audio
+        drop_text,  # cfg for text
+        mask: bool["b n"] | None = None,  # noqa: F722
+    ):
+        batch, seq_len = x.shape[0], x.shape[1]
+        if time.ndim == 0:
+            time = time.repeat(batch)
+
+        # t: conditioning time, c: context (text + masked cond audio), x: noised input audio
+        t = self.time_embed(time)
+        text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+        x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
+
+        # postfix time t to input x, [b n d] -> [b n+1 d]
+        x = torch.cat([t.unsqueeze(1), x], dim=1)  # pack t to x
+        if mask is not None:
+            mask = F.pad(mask, (1, 0), value=1)
+
+        rope = self.rotary_embed.forward_from_seq_len(seq_len + 1)
+
+        # flat unet transformer
+        skip_connect_type = self.skip_connect_type
+        skips = []
+        for idx, (maybe_skip_proj, attn_norm, attn, ff_norm, ff) in enumerate(self.layers):
+            layer = idx + 1
+
+            # skip connection logic
+            is_first_half = layer <= (self.depth // 2)
+            is_later_half = not is_first_half
+
+            if is_first_half:
+                skips.append(x)
+
+            if is_later_half:
+                skip = skips.pop()
+                if skip_connect_type == "concat":
+                    x = torch.cat((x, skip), dim=-1)
+                    x = maybe_skip_proj(x)
+                elif skip_connect_type == "add":
+                    x = x + skip
+
+            # attention and feedforward blocks
+            x = attn(attn_norm(x), rope=rope, mask=mask) + x
+            x = ff(ff_norm(x)) + x
+
+        assert len(skips) == 0
+
+        x = self.norm_out(x)[:, 1:, :]  # unpack t from x
+
+        return self.proj_out(x)

src/f5_tts/model/cfm.py

@@ -0,0 +1,287 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+from typing import Callable
+from random import random
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn.utils.rnn import pad_sequence
+
+from torchdiffeq import odeint
+
+from f5_tts.model.modules import MelSpec
+from f5_tts.model.utils import (
+    default,
+    exists,
+    list_str_to_idx,
+    list_str_to_tensor,
+    lens_to_mask,
+    mask_from_frac_lengths,
+)
+
+
+class CFM(nn.Module):
+    def __init__(
+        self,
+        transformer: nn.Module,
+        sigma=0.0,
+        odeint_kwargs: dict = dict(
+            # atol = 1e-5,
+            # rtol = 1e-5,
+            method="euler"  # 'midpoint'
+        ),
+        audio_drop_prob=0.3,
+        cond_drop_prob=0.2,
+        num_channels=None,
+        mel_spec_module: nn.Module | None = None,
+        mel_spec_kwargs: dict = dict(),
+        frac_lengths_mask: tuple[float, float] = (0.7, 1.0),
+        vocab_char_map: dict[str:int] | None = None,
+    ):
+        super().__init__()
+
+        self.frac_lengths_mask = frac_lengths_mask
+
+        # mel spec
+        self.mel_spec = default(mel_spec_module, MelSpec(**mel_spec_kwargs))
+        num_channels = default(num_channels, self.mel_spec.n_mel_channels)
+        self.num_channels = num_channels
+
+        # classifier-free guidance
+        self.audio_drop_prob = audio_drop_prob
+        self.cond_drop_prob = cond_drop_prob
+
+        # transformer
+        self.transformer = transformer
+        dim = transformer.dim
+        self.dim = dim
+
+        # conditional flow related
+        self.sigma = sigma
+
+        # sampling related
+        self.odeint_kwargs = odeint_kwargs
+
+        # vocab map for tokenization
+        self.vocab_char_map = vocab_char_map
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @torch.no_grad()
+    def sample(
+        self,
+        cond: float["b n d"] | float["b nw"],  # noqa: F722
+        text: int["b nt"] | list[str],  # noqa: F722
+        duration: int | int["b"],  # noqa: F821
+        *,
+        lens: int["b"] | None = None,  # noqa: F821
+        steps=32,
+        cfg_strength=1.0,
+        sway_sampling_coef=None,
+        seed: int | None = None,
+        max_duration=4096,
+        vocoder: Callable[[float["b d n"]], float["b nw"]] | None = None,  # noqa: F722
+        no_ref_audio=False,
+        duplicate_test=False,
+        t_inter=0.1,
+        edit_mask=None,
+    ):
+        self.eval()
+
+        if next(self.parameters()).dtype == torch.float16:
+            cond = cond.half()
+
+        # raw wave
+
+        if cond.ndim == 2:
+            cond = self.mel_spec(cond)
+            cond = cond.permute(0, 2, 1)
+            assert cond.shape[-1] == self.num_channels
+
+        batch, cond_seq_len, device = *cond.shape[:2], cond.device
+        if not exists(lens):
+            lens = torch.full((batch,), cond_seq_len, device=device, dtype=torch.long)
+
+        # text
+
+        if isinstance(text, list):
+            if exists(self.vocab_char_map):
+                text = list_str_to_idx(text, self.vocab_char_map).to(device)
+            else:
+                text = list_str_to_tensor(text).to(device)
+            assert text.shape[0] == batch
+
+        if exists(text):
+            text_lens = (text != -1).sum(dim=-1)
+            lens = torch.maximum(text_lens, lens)  # make sure lengths are at least those of the text characters
+
+        # duration
+
+        cond_mask = lens_to_mask(lens)
+        if edit_mask is not None:
+            cond_mask = cond_mask & edit_mask
+
+        if isinstance(duration, int):
+            duration = torch.full((batch,), duration, device=device, dtype=torch.long)
+
+        duration = torch.maximum(lens + 1, duration)  # just add one token so something is generated
+        duration = duration.clamp(max=max_duration)
+        max_duration = duration.amax()
+
+        # duplicate test corner for inner time step oberservation
+        if duplicate_test:
+            test_cond = F.pad(cond, (0, 0, cond_seq_len, max_duration - 2 * cond_seq_len), value=0.0)
+
+        cond = F.pad(cond, (0, 0, 0, max_duration - cond_seq_len), value=0.0)
+        cond_mask = F.pad(cond_mask, (0, max_duration - cond_mask.shape[-1]), value=False)
+        cond_mask = cond_mask.unsqueeze(-1)
+        step_cond = torch.where(
+            cond_mask, cond, torch.zeros_like(cond)
+        )  # allow direct control (cut cond audio) with lens passed in
+
+        if batch > 1:
+            mask = lens_to_mask(duration)
+        else:  # save memory and speed up, as single inference need no mask currently
+            mask = None
+
+        # test for no ref audio
+        if no_ref_audio:
+            cond = torch.zeros_like(cond)
+
+        # neural ode
+
+        def fn(t, x):
+            # at each step, conditioning is fixed
+            # step_cond = torch.where(cond_mask, cond, torch.zeros_like(cond))
+
+            # predict flow
+            pred = self.transformer(
+                x=x, cond=step_cond, text=text, time=t, mask=mask, drop_audio_cond=False, drop_text=False
+            )
+            if cfg_strength < 1e-5:
+                return pred
+
+            null_pred = self.transformer(
+                x=x, cond=step_cond, text=text, time=t, mask=mask, drop_audio_cond=True, drop_text=True
+            )
+            return pred + (pred - null_pred) * cfg_strength
+
+        # noise input
+        # to make sure batch inference result is same with different batch size, and for sure single inference
+        # still some difference maybe due to convolutional layers
+        y0 = []
+        for dur in duration:
+            if exists(seed):
+                torch.manual_seed(seed)
+            y0.append(torch.randn(dur, self.num_channels, device=self.device, dtype=step_cond.dtype))
+        y0 = pad_sequence(y0, padding_value=0, batch_first=True)
+
+        t_start = 0
+
+        # duplicate test corner for inner time step oberservation
+        if duplicate_test:
+            t_start = t_inter
+            y0 = (1 - t_start) * y0 + t_start * test_cond
+            steps = int(steps * (1 - t_start))
+
+        t = torch.linspace(t_start, 1, steps, device=self.device, dtype=step_cond.dtype)
+        if sway_sampling_coef is not None:
+            t = t + sway_sampling_coef * (torch.cos(torch.pi / 2 * t) - 1 + t)
+
+        trajectory = odeint(fn, y0, t, **self.odeint_kwargs)
+
+        sampled = trajectory[-1]
+        out = sampled
+        out = torch.where(cond_mask, cond, out)
+
+        if exists(vocoder):
+            out = out.permute(0, 2, 1)
+            out = vocoder(out)
+
+        return out, trajectory
+
+    def forward(
+        self,
+        inp: float["b n d"] | float["b nw"],  # mel or raw wave  # noqa: F722
+        text: int["b nt"] | list[str],  # noqa: F722
+        *,
+        lens: int["b"] | None = None,  # noqa: F821
+        noise_scheduler: str | None = None,
+    ):
+        # handle raw wave
+        if inp.ndim == 2:
+            inp = self.mel_spec(inp)
+            inp = inp.permute(0, 2, 1)
+            assert inp.shape[-1] == self.num_channels
+
+        batch, seq_len, dtype, device, _σ1 = *inp.shape[:2], inp.dtype, self.device, self.sigma
+
+        # handle text as string
+        if isinstance(text, list):
+            if exists(self.vocab_char_map):
+                text = list_str_to_idx(text, self.vocab_char_map).to(device)
+            else:
+                text = list_str_to_tensor(text).to(device)
+            assert text.shape[0] == batch
+
+        # lens and mask
+        if not exists(lens):
+            lens = torch.full((batch,), seq_len, device=device)
+
+        mask = lens_to_mask(lens, length=seq_len)  # useless here, as collate_fn will pad to max length in batch
+
+        # get a random span to mask out for training conditionally
+        frac_lengths = torch.zeros((batch,), device=self.device).float().uniform_(*self.frac_lengths_mask)
+        rand_span_mask = mask_from_frac_lengths(lens, frac_lengths)
+
+        if exists(mask):
+            rand_span_mask &= mask
+
+        # mel is x1
+        x1 = inp
+
+        # x0 is gaussian noise
+        x0 = torch.randn_like(x1)
+
+        # time step
+        time = torch.rand((batch,), dtype=dtype, device=self.device)
+        # TODO. noise_scheduler
+
+        # sample xt (φ_t(x) in the paper)
+        t = time.unsqueeze(-1).unsqueeze(-1)
+        φ = (1 - t) * x0 + t * x1
+        flow = x1 - x0
+
+        # only predict what is within the random mask span for infilling
+        cond = torch.where(rand_span_mask[..., None], torch.zeros_like(x1), x1)
+
+        # transformer and cfg training with a drop rate
+        drop_audio_cond = random() < self.audio_drop_prob  # p_drop in voicebox paper
+        if random() < self.cond_drop_prob:  # p_uncond in voicebox paper
+            drop_audio_cond = True
+            drop_text = True
+        else:
+            drop_text = False
+
+        # if want rigourously mask out padding, record in collate_fn in dataset.py, and pass in here
+        # adding mask will use more memory, thus also need to adjust batchsampler with scaled down threshold for long sequences
+        pred = self.transformer(
+            x=φ, cond=cond, text=text, time=time, drop_audio_cond=drop_audio_cond, drop_text=drop_text
+        )
+
+        # flow matching loss
+        loss = F.mse_loss(pred, flow, reduction="none")
+        loss = loss[rand_span_mask]
+
+        return loss.mean(), cond, pred

src/f5_tts/model/dataset.py

@@ -0,0 +1,296 @@
+import json
+import random
+from importlib.resources import files
+from tqdm import tqdm
+
+import torch
+import torch.nn.functional as F
+import torchaudio
+from torch import nn
+from torch.utils.data import Dataset, Sampler
+from datasets import load_from_disk
+from datasets import Dataset as Dataset_
+
+from f5_tts.model.modules import MelSpec
+from f5_tts.model.utils import default
+
+
+class HFDataset(Dataset):
+    def __init__(
+        self,
+        hf_dataset: Dataset,
+        target_sample_rate=24_000,
+        n_mel_channels=100,
+        hop_length=256,
+    ):
+        self.data = hf_dataset
+        self.target_sample_rate = target_sample_rate
+        self.hop_length = hop_length
+        self.mel_spectrogram = MelSpec(
+            target_sample_rate=target_sample_rate, n_mel_channels=n_mel_channels, hop_length=hop_length
+        )
+
+    def get_frame_len(self, index):
+        row = self.data[index]
+        audio = row["audio"]["array"]
+        sample_rate = row["audio"]["sampling_rate"]
+        return audio.shape[-1] / sample_rate * self.target_sample_rate / self.hop_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        row = self.data[index]
+        audio = row["audio"]["array"]
+
+        # logger.info(f"Audio shape: {audio.shape}")
+
+        sample_rate = row["audio"]["sampling_rate"]
+        duration = audio.shape[-1] / sample_rate
+
+        if duration > 30 or duration < 0.3:
+            return self.__getitem__((index + 1) % len(self.data))
+
+        audio_tensor = torch.from_numpy(audio).float()
+
+        if sample_rate != self.target_sample_rate:
+            resampler = torchaudio.transforms.Resample(sample_rate, self.target_sample_rate)
+            audio_tensor = resampler(audio_tensor)
+
+        audio_tensor = audio_tensor.unsqueeze(0)  # 't -> 1 t')
+
+        mel_spec = self.mel_spectrogram(audio_tensor)
+
+        mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t'
+
+        text = row["text"]
+
+        return dict(
+            mel_spec=mel_spec,
+            text=text,
+        )
+
+
+class CustomDataset(Dataset):
+    def __init__(
+        self,
+        custom_dataset: Dataset,
+        durations=None,
+        target_sample_rate=24_000,
+        hop_length=256,
+        n_mel_channels=100,
+        preprocessed_mel=False,
+        mel_spec_module: nn.Module | None = None,
+    ):
+        self.data = custom_dataset
+        self.durations = durations
+        self.target_sample_rate = target_sample_rate
+        self.hop_length = hop_length
+        self.preprocessed_mel = preprocessed_mel
+
+        if not preprocessed_mel:
+            self.mel_spectrogram = default(
+                mel_spec_module,
+                MelSpec(
+                    target_sample_rate=target_sample_rate,
+                    hop_length=hop_length,
+                    n_mel_channels=n_mel_channels,
+                ),
+            )
+
+    def get_frame_len(self, index):
+        if (
+            self.durations is not None
+        ):  # Please make sure the separately provided durations are correct, otherwise 99.99% OOM
+            return self.durations[index] * self.target_sample_rate / self.hop_length
+        return self.data[index]["duration"] * self.target_sample_rate / self.hop_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        row = self.data[index]
+        audio_path = row["audio_path"]
+        text = row["text"]
+        duration = row["duration"]
+
+        if self.preprocessed_mel:
+            mel_spec = torch.tensor(row["mel_spec"])
+
+        else:
+            audio, source_sample_rate = torchaudio.load(audio_path)
+            if audio.shape[0] > 1:
+                audio = torch.mean(audio, dim=0, keepdim=True)
+
+            if duration > 30 or duration < 0.3:
+                return self.__getitem__((index + 1) % len(self.data))
+
+            if source_sample_rate != self.target_sample_rate:
+                resampler = torchaudio.transforms.Resample(source_sample_rate, self.target_sample_rate)
+                audio = resampler(audio)
+
+            mel_spec = self.mel_spectrogram(audio)
+            mel_spec = mel_spec.squeeze(0)  # '1 d t -> d t')
+
+        return dict(
+            mel_spec=mel_spec,
+            text=text,
+        )
+
+
+# Dynamic Batch Sampler
+
+
+class DynamicBatchSampler(Sampler[list[int]]):
+    """Extension of Sampler that will do the following:
+    1.  Change the batch size (essentially number of sequences)
+        in a batch to ensure that the total number of frames are less
+        than a certain threshold.
+    2.  Make sure the padding efficiency in the batch is high.
+    """
+
+    def __init__(
+        self, sampler: Sampler[int], frames_threshold: int, max_samples=0, random_seed=None, drop_last: bool = False
+    ):
+        self.sampler = sampler
+        self.frames_threshold = frames_threshold
+        self.max_samples = max_samples
+
+        indices, batches = [], []
+        data_source = self.sampler.data_source
+
+        for idx in tqdm(
+            self.sampler, desc="Sorting with sampler... if slow, check whether dataset is provided with duration"
+        ):
+            indices.append((idx, data_source.get_frame_len(idx)))
+        indices.sort(key=lambda elem: elem[1])
+
+        batch = []
+        batch_frames = 0
+        for idx, frame_len in tqdm(
+            indices, desc=f"Creating dynamic batches with {frames_threshold} audio frames per gpu"
+        ):
+            if batch_frames + frame_len <= self.frames_threshold and (max_samples == 0 or len(batch) < max_samples):
+                batch.append(idx)
+                batch_frames += frame_len
+            else:
+                if len(batch) > 0:
+                    batches.append(batch)
+                if frame_len <= self.frames_threshold:
+                    batch = [idx]
+                    batch_frames = frame_len
+                else:
+                    batch = []
+                    batch_frames = 0
+
+        if not drop_last and len(batch) > 0:
+            batches.append(batch)
+
+        del indices
+
+        # if want to have different batches between epochs, may just set a seed and log it in ckpt
+        # cuz during multi-gpu training, although the batch on per gpu not change between epochs, the formed general minibatch is different
+        # e.g. for epoch n, use (random_seed + n)
+        random.seed(random_seed)
+        random.shuffle(batches)
+
+        self.batches = batches
+
+    def __iter__(self):
+        return iter(self.batches)
+
+    def __len__(self):
+        return len(self.batches)
+
+
+# Load dataset
+
+
+def load_dataset(
+    dataset_name: str,
+    tokenizer: str = "pinyin",
+    dataset_type: str = "CustomDataset",
+    audio_type: str = "raw",
+    mel_spec_module: nn.Module | None = None,
+    mel_spec_kwargs: dict = dict(),
+) -> CustomDataset | HFDataset:
+    """
+    dataset_type    - "CustomDataset" if you want to use tokenizer name and default data path to load for train_dataset
+                    - "CustomDatasetPath" if you just want to pass the full path to a preprocessed dataset without relying on tokenizer
+    """
+
+    print("Loading dataset ...")
+
+    if dataset_type == "CustomDataset":
+        rel_data_path = str(files("f5_tts").joinpath(f"../../data/{dataset_name}_{tokenizer}"))
+        if audio_type == "raw":
+            try:
+                train_dataset = load_from_disk(f"{rel_data_path}/raw")
+            except:  # noqa: E722
+                train_dataset = Dataset_.from_file(f"{rel_data_path}/raw.arrow")
+            preprocessed_mel = False
+        elif audio_type == "mel":
+            train_dataset = Dataset_.from_file(f"{rel_data_path}/mel.arrow")
+            preprocessed_mel = True
+        with open(f"{rel_data_path}/duration.json", "r", encoding="utf-8") as f:
+            data_dict = json.load(f)
+        durations = data_dict["duration"]
+        train_dataset = CustomDataset(
+            train_dataset,
+            durations=durations,
+            preprocessed_mel=preprocessed_mel,
+            mel_spec_module=mel_spec_module,
+            **mel_spec_kwargs,
+        )
+
+    elif dataset_type == "CustomDatasetPath":
+        try:
+            train_dataset = load_from_disk(f"{dataset_name}/raw")
+        except:  # noqa: E722
+            train_dataset = Dataset_.from_file(f"{dataset_name}/raw.arrow")
+
+        with open(f"{dataset_name}/duration.json", "r", encoding="utf-8") as f:
+            data_dict = json.load(f)
+        durations = data_dict["duration"]
+        train_dataset = CustomDataset(
+            train_dataset, durations=durations, preprocessed_mel=preprocessed_mel, **mel_spec_kwargs
+        )
+
+    elif dataset_type == "HFDataset":
+        print(
+            "Should manually modify the path of huggingface dataset to your need.\n"
+            + "May also the corresponding script cuz different dataset may have different format."
+        )
+        pre, post = dataset_name.split("_")
+        train_dataset = HFDataset(
+            load_dataset(f"{pre}/{pre}", split=f"train.{post}", cache_dir=str(files("f5_tts").joinpath("../../data"))),
+        )
+
+    return train_dataset
+
+
+# collation
+
+
+def collate_fn(batch):
+    mel_specs = [item["mel_spec"].squeeze(0) for item in batch]
+    mel_lengths = torch.LongTensor([spec.shape[-1] for spec in mel_specs])
+    max_mel_length = mel_lengths.amax()
+
+    padded_mel_specs = []
+    for spec in mel_specs:  # TODO. maybe records mask for attention here
+        padding = (0, max_mel_length - spec.size(-1))
+        padded_spec = F.pad(spec, padding, value=0)
+        padded_mel_specs.append(padded_spec)
+
+    mel_specs = torch.stack(padded_mel_specs)
+
+    text = [item["text"] for item in batch]
+    text_lengths = torch.LongTensor([len(item) for item in text])
+
+    return dict(
+        mel=mel_specs,
+        mel_lengths=mel_lengths,
+        text=text,
+        text_lengths=text_lengths,
+    )

src/f5_tts/model/modules.py

@@ -0,0 +1,581 @@
+"""
+ein notation:
+b - batch
+n - sequence
+nt - text sequence
+nw - raw wave length
+d - dimension
+"""
+
+from __future__ import annotations
+from typing import Optional
+import math
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torchaudio
+
+from x_transformers.x_transformers import apply_rotary_pos_emb
+
+
+# raw wav to mel spec
+
+
+class MelSpec(nn.Module):
+    def __init__(
+        self,
+        filter_length=1024,
+        hop_length=256,
+        win_length=1024,
+        n_mel_channels=100,
+        target_sample_rate=24_000,
+        normalize=False,
+        power=1,
+        norm=None,
+        center=True,
+    ):
+        super().__init__()
+        self.n_mel_channels = n_mel_channels
+
+        self.mel_stft = torchaudio.transforms.MelSpectrogram(
+            sample_rate=target_sample_rate,
+            n_fft=filter_length,
+            win_length=win_length,
+            hop_length=hop_length,
+            n_mels=n_mel_channels,
+            power=power,
+            center=center,
+            normalized=normalize,
+            norm=norm,
+        )
+
+        self.register_buffer("dummy", torch.tensor(0), persistent=False)
+
+    def forward(self, inp):
+        if len(inp.shape) == 3:
+            inp = inp.squeeze(1)  # 'b 1 nw -> b nw'
+
+        assert len(inp.shape) == 2
+
+        if self.dummy.device != inp.device:
+            self.to(inp.device)
+
+        mel = self.mel_stft(inp)
+        mel = mel.clamp(min=1e-5).log()
+        return mel
+
+
+# sinusoidal position embedding
+
+
+class SinusPositionEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x, scale=1000):
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
+        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+# convolutional position embedding
+
+
+class ConvPositionEmbedding(nn.Module):
+    def __init__(self, dim, kernel_size=31, groups=16):
+        super().__init__()
+        assert kernel_size % 2 != 0
+        self.conv1d = nn.Sequential(
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
+            nn.Mish(),
+            nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
+            nn.Mish(),
+        )
+
+    def forward(self, x: float["b n d"], mask: bool["b n"] | None = None):  # noqa: F722
+        if mask is not None:
+            mask = mask[..., None]
+            x = x.masked_fill(~mask, 0.0)
+
+        x = x.permute(0, 2, 1)
+        x = self.conv1d(x)
+        out = x.permute(0, 2, 1)
+
+        if mask is not None:
+            out = out.masked_fill(~mask, 0.0)
+
+        return out
+
+
+# rotary positional embedding related
+
+
+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
+    # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+    # has some connection to NTK literature
+    # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+    # https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
+    theta *= theta_rescale_factor ** (dim / (dim - 2))
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    freqs_cos = torch.cos(freqs)  # real part
+    freqs_sin = torch.sin(freqs)  # imaginary part
+    return torch.cat([freqs_cos, freqs_sin], dim=-1)
+
+
+def get_pos_embed_indices(start, length, max_pos, scale=1.0):
+    # length = length if isinstance(length, int) else length.max()
+    scale = scale * torch.ones_like(start, dtype=torch.float32)  # in case scale is a scalar
+    pos = (
+        start.unsqueeze(1)
+        + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long()
+    )
+    # avoid extra long error.
+    pos = torch.where(pos < max_pos, pos, max_pos - 1)
+    return pos
+
+
+# Global Response Normalization layer (Instance Normalization ?)
+
+
+class GRN(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
+        self.beta = nn.Parameter(torch.zeros(1, 1, dim))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=1, keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma * (x * Nx) + self.beta + x
+
+
+# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
+# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108
+
+
+class ConvNeXtV2Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        dilation: int = 1,
+    ):
+        super().__init__()
+        padding = (dilation * (7 - 1)) // 2
+        self.dwconv = nn.Conv1d(
+            dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
+        )  # depthwise conv
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, intermediate_dim)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.grn = GRN(intermediate_dim)
+        self.pwconv2 = nn.Linear(intermediate_dim, dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = x.transpose(1, 2)  # b n d -> b d n
+        x = self.dwconv(x)
+        x = x.transpose(1, 2)  # b d n -> b n d
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.grn(x)
+        x = self.pwconv2(x)
+        return residual + x
+
+
+# AdaLayerNormZero
+# return with modulated x for attn input, and params for later mlp modulation
+
+
+class AdaLayerNormZero(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(dim, dim * 6)
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb=None):
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)
+
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+# AdaLayerNormZero for final layer
+# return only with modulated x for attn input, cuz no more mlp modulation
+
+
+class AdaLayerNormZero_Final(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(dim, dim * 2)
+
+        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+
+    def forward(self, x, emb):
+        emb = self.linear(self.silu(emb))
+        scale, shift = torch.chunk(emb, 2, dim=1)
+
+        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
+        return x
+
+
+# FeedForward
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+
+        activation = nn.GELU(approximate=approximate)
+        project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
+        self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
+
+    def forward(self, x):
+        return self.ff(x)
+
+
+# Attention with possible joint part
+# modified from diffusers/src/diffusers/models/attention_processor.py
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        processor: JointAttnProcessor | AttnProcessor,
+        dim: int,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        context_dim: Optional[int] = None,  # if not None -> joint attention
+        context_pre_only=None,
+    ):
+        super().__init__()
+
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
+
+        self.processor = processor
+
+        self.dim = dim
+        self.heads = heads
+        self.inner_dim = dim_head * heads
+        self.dropout = dropout
+
+        self.context_dim = context_dim
+        self.context_pre_only = context_pre_only
+
+        self.to_q = nn.Linear(dim, self.inner_dim)
+        self.to_k = nn.Linear(dim, self.inner_dim)
+        self.to_v = nn.Linear(dim, self.inner_dim)
+
+        if self.context_dim is not None:
+            self.to_k_c = nn.Linear(context_dim, self.inner_dim)
+            self.to_v_c = nn.Linear(context_dim, self.inner_dim)
+            if self.context_pre_only is not None:
+                self.to_q_c = nn.Linear(context_dim, self.inner_dim)
+
+        self.to_out = nn.ModuleList([])
+        self.to_out.append(nn.Linear(self.inner_dim, dim))
+        self.to_out.append(nn.Dropout(dropout))
+
+        if self.context_pre_only is not None and not self.context_pre_only:
+            self.to_out_c = nn.Linear(self.inner_dim, dim)
+
+    def forward(
+        self,
+        x: float["b n d"],  # noised input x  # noqa: F722
+        c: float["b n d"] = None,  # context c  # noqa: F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        rope=None,  # rotary position embedding for x
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.Tensor:
+        if c is not None:
+            return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope)
+        else:
+            return self.processor(self, x, mask=mask, rope=rope)
+
+
+# Attention processor
+
+
+class AttnProcessor:
+    def __init__(self):
+        pass
+
+    def __call__(
+        self,
+        attn: Attention,
+        x: float["b n d"],  # noised input x  # noqa: F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        rope=None,  # rotary position embedding
+    ) -> torch.FloatTensor:
+        batch_size = x.shape[0]
+
+        # `sample` projections.
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+
+        # apply rotary position embedding
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+
+            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+
+        # attention
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # mask. e.g. inference got a batch with different target durations, mask out the padding
+        if mask is not None:
+            attn_mask = mask
+            attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
+            attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
+        else:
+            attn_mask = None
+
+        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
+        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        x = x.to(query.dtype)
+
+        # linear proj
+        x = attn.to_out[0](x)
+        # dropout
+        x = attn.to_out[1](x)
+
+        if mask is not None:
+            mask = mask.unsqueeze(-1)
+            x = x.masked_fill(~mask, 0.0)
+
+        return x
+
+
+# Joint Attention processor for MM-DiT
+# modified from diffusers/src/diffusers/models/attention_processor.py
+
+
+class JointAttnProcessor:
+    def __init__(self):
+        pass
+
+    def __call__(
+        self,
+        attn: Attention,
+        x: float["b n d"],  # noised input x  # noqa: F722
+        c: float["b nt d"] = None,  # context c, here text # noqa: F722
+        mask: bool["b n"] | None = None,  # noqa: F722
+        rope=None,  # rotary position embedding for x
+        c_rope=None,  # rotary position embedding for c
+    ) -> torch.FloatTensor:
+        residual = x
+
+        batch_size = c.shape[0]
+
+        # `sample` projections.
+        query = attn.to_q(x)
+        key = attn.to_k(x)
+        value = attn.to_v(x)
+
+        # `context` projections.
+        c_query = attn.to_q_c(c)
+        c_key = attn.to_k_c(c)
+        c_value = attn.to_v_c(c)
+
+        # apply rope for context and noised input independently
+        if rope is not None:
+            freqs, xpos_scale = rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+            query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
+            key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
+        if c_rope is not None:
+            freqs, xpos_scale = c_rope
+            q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
+            c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale)
+            c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale)
+
+        # attention
+        query = torch.cat([query, c_query], dim=1)
+        key = torch.cat([key, c_key], dim=1)
+        value = torch.cat([value, c_value], dim=1)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        # mask. e.g. inference got a batch with different target durations, mask out the padding
+        if mask is not None:
+            attn_mask = F.pad(mask, (0, c.shape[1]), value=True)  # no mask for c (text)
+            attn_mask = attn_mask.unsqueeze(1).unsqueeze(1)  # 'b n -> b 1 1 n'
+            attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
+        else:
+            attn_mask = None
+
+        x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
+        x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        x = x.to(query.dtype)
+
+        # Split the attention outputs.
+        x, c = (
+            x[:, : residual.shape[1]],
+            x[:, residual.shape[1] :],
+        )
+
+        # linear proj
+        x = attn.to_out[0](x)
+        # dropout
+        x = attn.to_out[1](x)
+        if not attn.context_pre_only:
+            c = attn.to_out_c(c)
+
+        if mask is not None:
+            mask = mask.unsqueeze(-1)
+            x = x.masked_fill(~mask, 0.0)
+            # c = c.masked_fill(~mask, 0.)  # no mask for c (text)
+
+        return x, c
+
+
+# DiT Block
+
+
+class DiTBlock(nn.Module):
+    def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1):
+        super().__init__()
+
+        self.attn_norm = AdaLayerNormZero(dim)
+        self.attn = Attention(
+            processor=AttnProcessor(),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+        )
+
+        self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+    def forward(self, x, t, mask=None, rope=None):  # x: noised input, t: time embedding
+        # pre-norm & modulation for attention input
+        norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)
+
+        # attention
+        attn_output = self.attn(x=norm, mask=mask, rope=rope)
+
+        # process attention output for input x
+        x = x + gate_msa.unsqueeze(1) * attn_output
+
+        norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        ff_output = self.ff(norm)
+        x = x + gate_mlp.unsqueeze(1) * ff_output
+
+        return x
+
+
+# MMDiT Block https://arxiv.org/abs/2403.03206
+
+
+class MMDiTBlock(nn.Module):
+    r"""
+    modified from diffusers/src/diffusers/models/attention.py
+
+    notes.
+    _c: context related. text, cond, etc. (left part in sd3 fig2.b)
+    _x: noised input related. (right part)
+    context_pre_only: last layer only do prenorm + modulation cuz no more ffn
+    """
+
+    def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_pre_only=False):
+        super().__init__()
+
+        self.context_pre_only = context_pre_only
+
+        self.attn_norm_c = AdaLayerNormZero_Final(dim) if context_pre_only else AdaLayerNormZero(dim)
+        self.attn_norm_x = AdaLayerNormZero(dim)
+        self.attn = Attention(
+            processor=JointAttnProcessor(),
+            dim=dim,
+            heads=heads,
+            dim_head=dim_head,
+            dropout=dropout,
+            context_dim=dim,
+            context_pre_only=context_pre_only,
+        )
+
+        if not context_pre_only:
+            self.ff_norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+            self.ff_c = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+        else:
+            self.ff_norm_c = None
+            self.ff_c = None
+        self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
+
+    def forward(self, x, c, t, mask=None, rope=None, c_rope=None):  # x: noised input, c: context, t: time embedding
+        # pre-norm & modulation for attention input
+        if self.context_pre_only:
+            norm_c = self.attn_norm_c(c, t)
+        else:
+            norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t)
+        norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t)
+
+        # attention
+        x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope)
+
+        # process attention output for context c
+        if self.context_pre_only:
+            c = None
+        else:  # if not last layer
+            c = c + c_gate_msa.unsqueeze(1) * c_attn_output
+
+            norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
+            c_ff_output = self.ff_c(norm_c)
+            c = c + c_gate_mlp.unsqueeze(1) * c_ff_output
+
+        # process attention output for input x
+        x = x + x_gate_msa.unsqueeze(1) * x_attn_output
+
+        norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
+        x_ff_output = self.ff_x(norm_x)
+        x = x + x_gate_mlp.unsqueeze(1) * x_ff_output
+
+        return c, x
+
+
+# time step conditioning embedding
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(self, dim, freq_embed_dim=256):
+        super().__init__()
+        self.time_embed = SinusPositionEmbedding(freq_embed_dim)
+        self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
+
+    def forward(self, timestep: float["b"]):  # noqa: F821
+        time_hidden = self.time_embed(timestep)
+        time_hidden = time_hidden.to(timestep.dtype)
+        time = self.time_mlp(time_hidden)  # b d
+        return time

src/f5_tts/model/trainer.py

@@ -0,0 +1,300 @@
+from __future__ import annotations
+
+import os
+import gc
+from tqdm import tqdm
+import wandb
+
+import torch
+from torch.optim import AdamW
+from torch.utils.data import DataLoader, Dataset, SequentialSampler
+from torch.optim.lr_scheduler import LinearLR, SequentialLR
+
+from accelerate import Accelerator
+from accelerate.utils import DistributedDataParallelKwargs
+
+from ema_pytorch import EMA
+
+from f5_tts.model import CFM
+from f5_tts.model.utils import exists, default
+from f5_tts.model.dataset import DynamicBatchSampler, collate_fn
+
+
+# trainer
+
+
+class Trainer:
+    def __init__(
+        self,
+        model: CFM,
+        epochs,
+        learning_rate,
+        num_warmup_updates=20000,
+        save_per_updates=1000,
+        checkpoint_path=None,
+        batch_size=32,
+        batch_size_type: str = "sample",
+        max_samples=32,
+        grad_accumulation_steps=1,
+        max_grad_norm=1.0,
+        noise_scheduler: str | None = None,
+        duration_predictor: torch.nn.Module | None = None,
+        wandb_project="test_e2-tts",
+        wandb_run_name="test_run",
+        wandb_resume_id: str = None,
+        last_per_steps=None,
+        accelerate_kwargs: dict = dict(),
+        ema_kwargs: dict = dict(),
+        bnb_optimizer: bool = False,
+    ):
+        ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+
+        logger = "wandb" if wandb.api.api_key else None
+        print(f"Using logger: {logger}")
+
+        self.accelerator = Accelerator(
+            log_with=logger,
+            kwargs_handlers=[ddp_kwargs],
+            gradient_accumulation_steps=grad_accumulation_steps,
+            **accelerate_kwargs,
+        )
+
+        if logger == "wandb":
+            if exists(wandb_resume_id):
+                init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name, "id": wandb_resume_id}}
+            else:
+                init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name}}
+            self.accelerator.init_trackers(
+                project_name=wandb_project,
+                init_kwargs=init_kwargs,
+                config={
+                    "epochs": epochs,
+                    "learning_rate": learning_rate,
+                    "num_warmup_updates": num_warmup_updates,
+                    "batch_size": batch_size,
+                    "batch_size_type": batch_size_type,
+                    "max_samples": max_samples,
+                    "grad_accumulation_steps": grad_accumulation_steps,
+                    "max_grad_norm": max_grad_norm,
+                    "gpus": self.accelerator.num_processes,
+                    "noise_scheduler": noise_scheduler,
+                },
+            )
+
+        self.model = model
+
+        if self.is_main:
+            self.ema_model = EMA(model, include_online_model=False, **ema_kwargs)
+
+            self.ema_model.to(self.accelerator.device)
+
+        self.epochs = epochs
+        self.num_warmup_updates = num_warmup_updates
+        self.save_per_updates = save_per_updates
+        self.last_per_steps = default(last_per_steps, save_per_updates * grad_accumulation_steps)
+        self.checkpoint_path = default(checkpoint_path, "ckpts/test_e2-tts")
+
+        self.batch_size = batch_size
+        self.batch_size_type = batch_size_type
+        self.max_samples = max_samples
+        self.grad_accumulation_steps = grad_accumulation_steps
+        self.max_grad_norm = max_grad_norm
+
+        self.noise_scheduler = noise_scheduler
+
+        self.duration_predictor = duration_predictor
+
+        if bnb_optimizer:
+            import bitsandbytes as bnb
+
+            self.optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=learning_rate)
+        else:
+            self.optimizer = AdamW(model.parameters(), lr=learning_rate)
+        self.model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer)
+
+    @property
+    def is_main(self):
+        return self.accelerator.is_main_process
+
+    def save_checkpoint(self, step, last=False):
+        self.accelerator.wait_for_everyone()
+        if self.is_main:
+            checkpoint = dict(
+                model_state_dict=self.accelerator.unwrap_model(self.model).state_dict(),
+                optimizer_state_dict=self.accelerator.unwrap_model(self.optimizer).state_dict(),
+                ema_model_state_dict=self.ema_model.state_dict(),
+                scheduler_state_dict=self.scheduler.state_dict(),
+                step=step,
+            )
+            if not os.path.exists(self.checkpoint_path):
+                os.makedirs(self.checkpoint_path)
+            if last:
+                self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_last.pt")
+                print(f"Saved last checkpoint at step {step}")
+            else:
+                self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_{step}.pt")
+
+    def load_checkpoint(self):
+        if (
+            not exists(self.checkpoint_path)
+            or not os.path.exists(self.checkpoint_path)
+            or not os.listdir(self.checkpoint_path)
+        ):
+            return 0
+
+        self.accelerator.wait_for_everyone()
+        if "model_last.pt" in os.listdir(self.checkpoint_path):
+            latest_checkpoint = "model_last.pt"
+        else:
+            latest_checkpoint = sorted(
+                [f for f in os.listdir(self.checkpoint_path) if f.endswith(".pt")],
+                key=lambda x: int("".join(filter(str.isdigit, x))),
+            )[-1]
+        # checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location=self.accelerator.device)  # rather use accelerator.load_state ಥ_ಥ
+        checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", weights_only=True, map_location="cpu")
+
+        if self.is_main:
+            self.ema_model.load_state_dict(checkpoint["ema_model_state_dict"])
+
+        if "step" in checkpoint:
+            self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"])
+            self.accelerator.unwrap_model(self.optimizer).load_state_dict(checkpoint["optimizer_state_dict"])
+            if self.scheduler:
+                self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
+            step = checkpoint["step"]
+        else:
+            checkpoint["model_state_dict"] = {
+                k.replace("ema_model.", ""): v
+                for k, v in checkpoint["ema_model_state_dict"].items()
+                if k not in ["initted", "step"]
+            }
+            self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"])
+            step = 0
+
+        del checkpoint
+        gc.collect()
+        return step
+
+    def train(self, train_dataset: Dataset, num_workers=16, resumable_with_seed: int = None):
+        if exists(resumable_with_seed):
+            generator = torch.Generator()
+            generator.manual_seed(resumable_with_seed)
+        else:
+            generator = None
+
+        if self.batch_size_type == "sample":
+            train_dataloader = DataLoader(
+                train_dataset,
+                collate_fn=collate_fn,
+                num_workers=num_workers,
+                pin_memory=True,
+                persistent_workers=True,
+                batch_size=self.batch_size,
+                shuffle=True,
+                generator=generator,
+            )
+        elif self.batch_size_type == "frame":
+            self.accelerator.even_batches = False
+            sampler = SequentialSampler(train_dataset)
+            batch_sampler = DynamicBatchSampler(
+                sampler, self.batch_size, max_samples=self.max_samples, random_seed=resumable_with_seed, drop_last=False
+            )
+            train_dataloader = DataLoader(
+                train_dataset,
+                collate_fn=collate_fn,
+                num_workers=num_workers,
+                pin_memory=True,
+                persistent_workers=True,
+                batch_sampler=batch_sampler,
+            )
+        else:
+            raise ValueError(f"batch_size_type must be either 'sample' or 'frame', but received {self.batch_size_type}")
+
+        #  accelerator.prepare() dispatches batches to devices;
+        #  which means the length of dataloader calculated before, should consider the number of devices
+        warmup_steps = (
+            self.num_warmup_updates * self.accelerator.num_processes
+        )  # consider a fixed warmup steps while using accelerate multi-gpu ddp
+        # otherwise by default with split_batches=False, warmup steps change with num_processes
+        total_steps = len(train_dataloader) * self.epochs / self.grad_accumulation_steps
+        decay_steps = total_steps - warmup_steps
+        warmup_scheduler = LinearLR(self.optimizer, start_factor=1e-8, end_factor=1.0, total_iters=warmup_steps)
+        decay_scheduler = LinearLR(self.optimizer, start_factor=1.0, end_factor=1e-8, total_iters=decay_steps)
+        self.scheduler = SequentialLR(
+            self.optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[warmup_steps]
+        )
+        train_dataloader, self.scheduler = self.accelerator.prepare(
+            train_dataloader, self.scheduler
+        )  # actual steps = 1 gpu steps / gpus
+        start_step = self.load_checkpoint()
+        global_step = start_step
+
+        if exists(resumable_with_seed):
+            orig_epoch_step = len(train_dataloader)
+            skipped_epoch = int(start_step // orig_epoch_step)
+            skipped_batch = start_step % orig_epoch_step
+            skipped_dataloader = self.accelerator.skip_first_batches(train_dataloader, num_batches=skipped_batch)
+        else:
+            skipped_epoch = 0
+
+        for epoch in range(skipped_epoch, self.epochs):
+            self.model.train()
+            if exists(resumable_with_seed) and epoch == skipped_epoch:
+                progress_bar = tqdm(
+                    skipped_dataloader,
+                    desc=f"Epoch {epoch+1}/{self.epochs}",
+                    unit="step",
+                    disable=not self.accelerator.is_local_main_process,
+                    initial=skipped_batch,
+                    total=orig_epoch_step,
+                )
+            else:
+                progress_bar = tqdm(
+                    train_dataloader,
+                    desc=f"Epoch {epoch+1}/{self.epochs}",
+                    unit="step",
+                    disable=not self.accelerator.is_local_main_process,
+                )
+
+            for batch in progress_bar:
+                with self.accelerator.accumulate(self.model):
+                    text_inputs = batch["text"]
+                    mel_spec = batch["mel"].permute(0, 2, 1)
+                    mel_lengths = batch["mel_lengths"]
+
+                    # TODO. add duration predictor training
+                    if self.duration_predictor is not None and self.accelerator.is_local_main_process:
+                        dur_loss = self.duration_predictor(mel_spec, lens=batch.get("durations"))
+                        self.accelerator.log({"duration loss": dur_loss.item()}, step=global_step)
+
+                    loss, cond, pred = self.model(
+                        mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler
+                    )
+                    self.accelerator.backward(loss)
+
+                    if self.max_grad_norm > 0 and self.accelerator.sync_gradients:
+                        self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+
+                    self.optimizer.step()
+                    self.scheduler.step()
+                    self.optimizer.zero_grad()
+
+                if self.is_main:
+                    self.ema_model.update()
+
+                global_step += 1
+
+                if self.accelerator.is_local_main_process:
+                    self.accelerator.log({"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_step)
+
+                progress_bar.set_postfix(step=str(global_step), loss=loss.item())
+
+                if global_step % (self.save_per_updates * self.grad_accumulation_steps) == 0:
+                    self.save_checkpoint(global_step)
+
+                if global_step % self.last_per_steps == 0:
+                    self.save_checkpoint(global_step, last=True)
+
+        self.save_checkpoint(global_step, last=True)
+
+        self.accelerator.end_training()

src/f5_tts/model/utils.py

@@ -0,0 +1,185 @@
+from __future__ import annotations
+
+import os
+import random
+from collections import defaultdict
+from importlib.resources import files
+
+import torch
+from torch.nn.utils.rnn import pad_sequence
+
+import jieba
+from pypinyin import lazy_pinyin, Style
+
+
+# seed everything
+
+
+def seed_everything(seed=0):
+    random.seed(seed)
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+# helpers
+
+
+def exists(v):
+    return v is not None
+
+
+def default(v, d):
+    return v if exists(v) else d
+
+
+# tensor helpers
+
+
+def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]:  # noqa: F722 F821
+    if not exists(length):
+        length = t.amax()
+
+    seq = torch.arange(length, device=t.device)
+    return seq[None, :] < t[:, None]
+
+
+def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]):  # noqa: F722 F821
+    max_seq_len = seq_len.max().item()
+    seq = torch.arange(max_seq_len, device=start.device).long()
+    start_mask = seq[None, :] >= start[:, None]
+    end_mask = seq[None, :] < end[:, None]
+    return start_mask & end_mask
+
+
+def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]):  # noqa: F722 F821
+    lengths = (frac_lengths * seq_len).long()
+    max_start = seq_len - lengths
+
+    rand = torch.rand_like(frac_lengths)
+    start = (max_start * rand).long().clamp(min=0)
+    end = start + lengths
+
+    return mask_from_start_end_indices(seq_len, start, end)
+
+
+def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]:  # noqa: F722
+    if not exists(mask):
+        return t.mean(dim=1)
+
+    t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device))
+    num = t.sum(dim=1)
+    den = mask.float().sum(dim=1)
+
+    return num / den.clamp(min=1.0)
+
+
+# simple utf-8 tokenizer, since paper went character based
+def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]:  # noqa: F722
+    list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text]  # ByT5 style
+    text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True)
+    return text
+
+
+# char tokenizer, based on custom dataset's extracted .txt file
+def list_str_to_idx(
+    text: list[str] | list[list[str]],
+    vocab_char_map: dict[str, int],  # {char: idx}
+    padding_value=-1,
+) -> int["b nt"]:  # noqa: F722
+    list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text]  # pinyin or char style
+    text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True)
+    return text
+
+
+# Get tokenizer
+
+
+def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
+    """
+    tokenizer   - "pinyin" do g2p for only chinese characters, need .txt vocab_file
+                - "char" for char-wise tokenizer, need .txt vocab_file
+                - "byte" for utf-8 tokenizer
+                - "custom" if you're directly passing in a path to the vocab.txt you want to use
+    vocab_size  - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols
+                - if use "char", derived from unfiltered character & symbol counts of custom dataset
+                - if use "byte", set to 256 (unicode byte range)
+    """
+    if tokenizer in ["pinyin", "char"]:
+        tokenizer_path = os.path.join(files("f5_tts").joinpath("../../data"), f"{dataset_name}_{tokenizer}/vocab.txt")
+        with open(tokenizer_path, "r", encoding="utf-8") as f:
+            vocab_char_map = {}
+            for i, char in enumerate(f):
+                vocab_char_map[char[:-1]] = i
+        vocab_size = len(vocab_char_map)
+        assert vocab_char_map[" "] == 0, "make sure space is of idx 0 in vocab.txt, cuz 0 is used for unknown char"
+
+    elif tokenizer == "byte":
+        vocab_char_map = None
+        vocab_size = 256
+
+    elif tokenizer == "custom":
+        with open(dataset_name, "r", encoding="utf-8") as f:
+            vocab_char_map = {}
+            for i, char in enumerate(f):
+                vocab_char_map[char[:-1]] = i
+        vocab_size = len(vocab_char_map)
+
+    return vocab_char_map, vocab_size
+
+
+# convert char to pinyin
+
+
+def convert_char_to_pinyin(text_list, polyphone=True):
+    final_text_list = []
+    god_knows_why_en_testset_contains_zh_quote = str.maketrans(
+        {"“": '"', "”": '"', "‘": "'", "’": "'"}
+    )  # in case librispeech (orig no-pc) test-clean
+    custom_trans = str.maketrans({";": ","})  # add custom trans here, to address oov
+    for text in text_list:
+        char_list = []
+        text = text.translate(god_knows_why_en_testset_contains_zh_quote)
+        text = text.translate(custom_trans)
+        for seg in jieba.cut(text):
+            seg_byte_len = len(bytes(seg, "UTF-8"))
+            if seg_byte_len == len(seg):  # if pure alphabets and symbols
+                if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":
+                    char_list.append(" ")
+                char_list.extend(seg)
+            elif polyphone and seg_byte_len == 3 * len(seg):  # if pure chinese characters
+                seg = lazy_pinyin(seg, style=Style.TONE3, tone_sandhi=True)
+                for c in seg:
+                    if c not in "。，、；：？！《》【】—…":
+                        char_list.append(" ")
+                    char_list.append(c)
+            else:  # if mixed chinese characters, alphabets and symbols
+                for c in seg:
+                    if ord(c) < 256:
+                        char_list.extend(c)
+                    else:
+                        if c not in "。，、；：？！《》【】—…":
+                            char_list.append(" ")
+                            char_list.extend(lazy_pinyin(c, style=Style.TONE3, tone_sandhi=True))
+                        else:  # if is zh punc
+                            char_list.append(c)
+        final_text_list.append(char_list)
+
+    return final_text_list
+
+
+# filter func for dirty data with many repetitions
+
+
+def repetition_found(text, length=2, tolerance=10):
+    pattern_count = defaultdict(int)
+    for i in range(len(text) - length + 1):
+        pattern = text[i : i + length]
+        pattern_count[pattern] += 1
+    for pattern, count in pattern_count.items():
+        if count > tolerance:
+            return True
+    return False

src/f5_tts/scripts/count_max_epoch.py

@@ -0,0 +1,33 @@
+"""ADAPTIVE BATCH SIZE"""
+
+print("Adaptive batch size: using grouping batch sampler, frames_per_gpu fixed fed in")
+print("  -> least padding, gather wavs with accumulated frames in a batch\n")
+
+# data
+total_hours = 95282
+mel_hop_length = 256
+mel_sampling_rate = 24000
+
+# target
+wanted_max_updates = 1000000
+
+# train params
+gpus = 8
+frames_per_gpu = 38400  # 8 * 38400 = 307200
+grad_accum = 1
+
+# intermediate
+mini_batch_frames = frames_per_gpu * grad_accum * gpus
+mini_batch_hours = mini_batch_frames * mel_hop_length / mel_sampling_rate / 3600
+updates_per_epoch = total_hours / mini_batch_hours
+steps_per_epoch = updates_per_epoch * grad_accum
+
+# result
+epochs = wanted_max_updates / updates_per_epoch
+print(f"epochs should be set to: {epochs:.0f} ({epochs/grad_accum:.1f} x gd_acum {grad_accum})")
+print(f"progress_bar should show approx. 0/{updates_per_epoch:.0f} updates")
+print(f"                      or approx. 0/{steps_per_epoch:.0f} steps")
+
+# others
+print(f"total {total_hours:.0f} hours")
+print(f"mini-batch of {mini_batch_frames:.0f} frames, {mini_batch_hours:.2f} hours per mini-batch")

src/f5_tts/scripts/count_params_gflops.py

@@ -0,0 +1,39 @@
+import sys
+import os
+
+sys.path.append(os.getcwd())
+
+from f5_tts.model import CFM, DiT
+
+import torch
+import thop
+
+
+""" ~155M """
+# transformer =     UNetT(dim = 768, depth = 20, heads = 12, ff_mult = 4)
+# transformer =     UNetT(dim = 768, depth = 20, heads = 12, ff_mult = 4, text_dim = 512, conv_layers = 4)
+# transformer =       DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2)
+# transformer =       DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2, text_dim = 512, conv_layers = 4)
+# transformer =       DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2, text_dim = 512, conv_layers = 4, long_skip_connection = True)
+# transformer =     MMDiT(dim = 512, depth = 16, heads = 16, ff_mult = 2)
+
+""" ~335M """
+# FLOPs: 622.1 G, Params: 333.2 M
+# transformer =     UNetT(dim = 1024, depth = 24, heads = 16, ff_mult = 4)
+# FLOPs: 363.4 G, Params: 335.8 M
+transformer = DiT(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+
+
+model = CFM(transformer=transformer)
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+duration = 20
+frame_length = int(duration * target_sample_rate / hop_length)
+text_length = 150
+
+flops, params = thop.profile(
+    model, inputs=(torch.randn(1, frame_length, n_mel_channels), torch.zeros(1, text_length, dtype=torch.long))
+)
+print(f"FLOPs: {flops / 1e9} G")
+print(f"Params: {params / 1e6} M")

src/f5_tts/train/README.md

@@ -0,0 +1,69 @@
+# Training
+
+## Prepare Dataset
+
+Example data processing scripts for Emilia and Wenetspeech4TTS, and you may tailor your own one along with a Dataset class in `src/f5_tts/model/dataset.py`.
+
+### 1. Datasets used for pretrained models
+Download corresponding dataset first, and fill in the path in scripts.
+
+```bash
+# Prepare the Emilia dataset
+python src/f5_tts/train/datasets/prepare_emilia.py
+
+# Prepare the Wenetspeech4TTS dataset
+python src/f5_tts/train/datasets/prepare_wenetspeech4tts.py
+```
+
+### 2. Create custom dataset with metadata.csv
+Use guidance see [#57 here](https://github.com/SWivid/F5-TTS/discussions/57#discussioncomment-10959029).
+
+```bash
+python src/f5_tts/train/datasets/prepare_csv_wavs.py
+```
+
+## Training & Finetuning
+
+Once your datasets are prepared, you can start the training process.
+
+### 1. Training script used for pretrained model
+
+```bash
+# setup accelerate config, e.g. use multi-gpu ddp, fp16
+# will be to: ~/.cache/huggingface/accelerate/default_config.yaml     
+accelerate config
+accelerate launch src/f5_tts/train/train.py
+```
+
+### 2. Finetuning practice
+Discussion board for Finetuning [#57](https://github.com/SWivid/F5-TTS/discussions/57).
+
+Gradio UI training/finetuning with `src/f5_tts/train/finetune_gradio.py` see [#143](https://github.com/SWivid/F5-TTS/discussions/143).
+
+### 3. Wandb Logging
+
+The `wandb/` dir will be created under path you run training/finetuning scripts.
+
+By default, the training script does NOT use logging (assuming you didn't manually log in using `wandb login`).
+
+To turn on wandb logging, you can either:
+
+1. Manually login with `wandb login`: Learn more [here](https://docs.wandb.ai/ref/cli/wandb-login)
+2. Automatically login programmatically by setting an environment variable: Get an API KEY at https://wandb.ai/site/ and set the environment variable as follows:
+
+On Mac & Linux:
+
+```
+export WANDB_API_KEY=<YOUR WANDB API KEY>
+```
+
+On Windows:
+
+```
+set WANDB_API_KEY=<YOUR WANDB API KEY>
+```
+Moreover, if you couldn't access Wandb and want to log metrics offline, you can the environment variable as follows:
+
+```
+export WANDB_MODE=offline
+```

src/f5_tts/train/datasets/prepare_csv_wavs.py

@@ -0,0 +1,140 @@
+import os
+import sys
+
+sys.path.append(os.getcwd())
+
+import argparse
+import csv
+import json
+import shutil
+from importlib.resources import files
+from pathlib import Path
+
+import torchaudio
+from tqdm import tqdm
+from datasets.arrow_writer import ArrowWriter
+
+from f5_tts.model.utils import (
+    convert_char_to_pinyin,
+)
+
+
+PRETRAINED_VOCAB_PATH = files("f5_tts").joinpath("../../data/Emilia_ZH_EN_pinyin/vocab.txt")
+
+
+def is_csv_wavs_format(input_dataset_dir):
+    fpath = Path(input_dataset_dir)
+    metadata = fpath / "metadata.csv"
+    wavs = fpath / "wavs"
+    return metadata.exists() and metadata.is_file() and wavs.exists() and wavs.is_dir()
+
+
+def prepare_csv_wavs_dir(input_dir):
+    assert is_csv_wavs_format(input_dir), f"not csv_wavs format: {input_dir}"
+    input_dir = Path(input_dir)
+    metadata_path = input_dir / "metadata.csv"
+    audio_path_text_pairs = read_audio_text_pairs(metadata_path.as_posix())
+
+    sub_result, durations = [], []
+    vocab_set = set()
+    polyphone = True
+    for audio_path, text in audio_path_text_pairs:
+        if not Path(audio_path).exists():
+            print(f"audio {audio_path} not found, skipping")
+            continue
+        audio_duration = get_audio_duration(audio_path)
+        # assume tokenizer = "pinyin"  ("pinyin" | "char")
+        text = convert_char_to_pinyin([text], polyphone=polyphone)[0]
+        sub_result.append({"audio_path": audio_path, "text": text, "duration": audio_duration})
+        durations.append(audio_duration)
+        vocab_set.update(list(text))
+
+    return sub_result, durations, vocab_set
+
+
+def get_audio_duration(audio_path):
+    audio, sample_rate = torchaudio.load(audio_path)
+    num_channels = audio.shape[0]
+    return audio.shape[1] / (sample_rate * num_channels)
+
+
+def read_audio_text_pairs(csv_file_path):
+    audio_text_pairs = []
+
+    parent = Path(csv_file_path).parent
+    with open(csv_file_path, mode="r", newline="", encoding="utf-8-sig") as csvfile:
+        reader = csv.reader(csvfile, delimiter="|")
+        next(reader)  # Skip the header row
+        for row in reader:
+            if len(row) >= 2:
+                audio_file = row[0].strip()  # First column: audio file path
+                text = row[1].strip()  # Second column: text
+                audio_file_path = parent / audio_file
+                audio_text_pairs.append((audio_file_path.as_posix(), text))
+
+    return audio_text_pairs
+
+
+def save_prepped_dataset(out_dir, result, duration_list, text_vocab_set, is_finetune):
+    out_dir = Path(out_dir)
+    # save preprocessed dataset to disk
+    out_dir.mkdir(exist_ok=True, parents=True)
+    print(f"\nSaving to {out_dir} ...")
+
+    # dataset = Dataset.from_dict({"audio_path": audio_path_list, "text": text_list, "duration": duration_list})  # oom
+    # dataset.save_to_disk(f"{out_dir}/raw", max_shard_size="2GB")
+    raw_arrow_path = out_dir / "raw.arrow"
+    with ArrowWriter(path=raw_arrow_path.as_posix(), writer_batch_size=1) as writer:
+        for line in tqdm(result, desc="Writing to raw.arrow ..."):
+            writer.write(line)
+
+    # dup a json separately saving duration in case for DynamicBatchSampler ease
+    dur_json_path = out_dir / "duration.json"
+    with open(dur_json_path.as_posix(), "w", encoding="utf-8") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    # vocab map, i.e. tokenizer
+    # add alphabets and symbols (optional, if plan to ft on de/fr etc.)
+    # if tokenizer == "pinyin":
+    #     text_vocab_set.update([chr(i) for i in range(32, 127)] + [chr(i) for i in range(192, 256)])
+    voca_out_path = out_dir / "vocab.txt"
+    with open(voca_out_path.as_posix(), "w") as f:
+        for vocab in sorted(text_vocab_set):
+            f.write(vocab + "\n")
+
+    if is_finetune:
+        file_vocab_finetune = PRETRAINED_VOCAB_PATH.as_posix()
+        shutil.copy2(file_vocab_finetune, voca_out_path)
+    else:
+        with open(voca_out_path, "w") as f:
+            for vocab in sorted(text_vocab_set):
+                f.write(vocab + "\n")
+
+    dataset_name = out_dir.stem
+    print(f"\nFor {dataset_name}, sample count: {len(result)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}")
+    print(f"For {dataset_name}, total {sum(duration_list)/3600:.2f} hours")
+
+
+def prepare_and_save_set(inp_dir, out_dir, is_finetune: bool = True):
+    if is_finetune:
+        assert PRETRAINED_VOCAB_PATH.exists(), f"pretrained vocab.txt not found: {PRETRAINED_VOCAB_PATH}"
+    sub_result, durations, vocab_set = prepare_csv_wavs_dir(inp_dir)
+    save_prepped_dataset(out_dir, sub_result, durations, vocab_set, is_finetune)
+
+
+def cli():
+    # finetune: python scripts/prepare_csv_wavs.py /path/to/input_dir /path/to/output_dir_pinyin
+    # pretrain: python scripts/prepare_csv_wavs.py /path/to/output_dir_pinyin --pretrain
+    parser = argparse.ArgumentParser(description="Prepare and save dataset.")
+    parser.add_argument("inp_dir", type=str, help="Input directory containing the data.")
+    parser.add_argument("out_dir", type=str, help="Output directory to save the prepared data.")
+    parser.add_argument("--pretrain", action="store_true", help="Enable for new pretrain, otherwise is a fine-tune")
+
+    args = parser.parse_args()
+
+    prepare_and_save_set(args.inp_dir, args.out_dir, is_finetune=not args.pretrain)
+
+
+if __name__ == "__main__":
+    cli()

src/f5_tts/train/datasets/prepare_emilia.py

@@ -0,0 +1,230 @@
+# Emilia Dataset: https://huggingface.co/datasets/amphion/Emilia-Dataset/tree/fc71e07
+# if use updated new version, i.e. WebDataset, feel free to modify / draft your own script
+
+# generate audio text map for Emilia ZH & EN
+# evaluate for vocab size
+
+import os
+import sys
+
+sys.path.append(os.getcwd())
+
+import json
+from concurrent.futures import ProcessPoolExecutor
+from importlib.resources import files
+from pathlib import Path
+from tqdm import tqdm
+
+from datasets.arrow_writer import ArrowWriter
+
+from f5_tts.model.utils import (
+    repetition_found,
+    convert_char_to_pinyin,
+)
+
+
+out_zh = {
+    "ZH_B00041_S06226",
+    "ZH_B00042_S09204",
+    "ZH_B00065_S09430",
+    "ZH_B00065_S09431",
+    "ZH_B00066_S09327",
+    "ZH_B00066_S09328",
+}
+zh_filters = ["い", "て"]
+# seems synthesized audios, or heavily code-switched
+out_en = {
+    "EN_B00013_S00913",
+    "EN_B00042_S00120",
+    "EN_B00055_S04111",
+    "EN_B00061_S00693",
+    "EN_B00061_S01494",
+    "EN_B00061_S03375",
+    "EN_B00059_S00092",
+    "EN_B00111_S04300",
+    "EN_B00100_S03759",
+    "EN_B00087_S03811",
+    "EN_B00059_S00950",
+    "EN_B00089_S00946",
+    "EN_B00078_S05127",
+    "EN_B00070_S04089",
+    "EN_B00074_S09659",
+    "EN_B00061_S06983",
+    "EN_B00061_S07060",
+    "EN_B00059_S08397",
+    "EN_B00082_S06192",
+    "EN_B00091_S01238",
+    "EN_B00089_S07349",
+    "EN_B00070_S04343",
+    "EN_B00061_S02400",
+    "EN_B00076_S01262",
+    "EN_B00068_S06467",
+    "EN_B00076_S02943",
+    "EN_B00064_S05954",
+    "EN_B00061_S05386",
+    "EN_B00066_S06544",
+    "EN_B00076_S06944",
+    "EN_B00072_S08620",
+    "EN_B00076_S07135",
+    "EN_B00076_S09127",
+    "EN_B00065_S00497",
+    "EN_B00059_S06227",
+    "EN_B00063_S02859",
+    "EN_B00075_S01547",
+    "EN_B00061_S08286",
+    "EN_B00079_S02901",
+    "EN_B00092_S03643",
+    "EN_B00096_S08653",
+    "EN_B00063_S04297",
+    "EN_B00063_S04614",
+    "EN_B00079_S04698",
+    "EN_B00104_S01666",
+    "EN_B00061_S09504",
+    "EN_B00061_S09694",
+    "EN_B00065_S05444",
+    "EN_B00063_S06860",
+    "EN_B00065_S05725",
+    "EN_B00069_S07628",
+    "EN_B00083_S03875",
+    "EN_B00071_S07665",
+    "EN_B00071_S07665",
+    "EN_B00062_S04187",
+    "EN_B00065_S09873",
+    "EN_B00065_S09922",
+    "EN_B00084_S02463",
+    "EN_B00067_S05066",
+    "EN_B00106_S08060",
+    "EN_B00073_S06399",
+    "EN_B00073_S09236",
+    "EN_B00087_S00432",
+    "EN_B00085_S05618",
+    "EN_B00064_S01262",
+    "EN_B00072_S01739",
+    "EN_B00059_S03913",
+    "EN_B00069_S04036",
+    "EN_B00067_S05623",
+    "EN_B00060_S05389",
+    "EN_B00060_S07290",
+    "EN_B00062_S08995",
+}
+en_filters = ["ا", "い", "て"]
+
+
+def deal_with_audio_dir(audio_dir):
+    audio_jsonl = audio_dir.with_suffix(".jsonl")
+    sub_result, durations = [], []
+    vocab_set = set()
+    bad_case_zh = 0
+    bad_case_en = 0
+    with open(audio_jsonl, "r") as f:
+        lines = f.readlines()
+        for line in tqdm(lines, desc=f"{audio_jsonl.stem}"):
+            obj = json.loads(line)
+            text = obj["text"]
+            if obj["language"] == "zh":
+                if obj["wav"].split("/")[1] in out_zh or any(f in text for f in zh_filters) or repetition_found(text):
+                    bad_case_zh += 1
+                    continue
+                else:
+                    text = text.translate(
+                        str.maketrans({",": "，", "!": "！", "?": "？"})
+                    )  # not "。" cuz much code-switched
+            if obj["language"] == "en":
+                if (
+                    obj["wav"].split("/")[1] in out_en
+                    or any(f in text for f in en_filters)
+                    or repetition_found(text, length=4)
+                ):
+                    bad_case_en += 1
+                    continue
+            if tokenizer == "pinyin":
+                text = convert_char_to_pinyin([text], polyphone=polyphone)[0]
+            duration = obj["duration"]
+            sub_result.append({"audio_path": str(audio_dir.parent / obj["wav"]), "text": text, "duration": duration})
+            durations.append(duration)
+            vocab_set.update(list(text))
+    return sub_result, durations, vocab_set, bad_case_zh, bad_case_en
+
+
+def main():
+    assert tokenizer in ["pinyin", "char"]
+    result = []
+    duration_list = []
+    text_vocab_set = set()
+    total_bad_case_zh = 0
+    total_bad_case_en = 0
+
+    # process raw data
+    executor = ProcessPoolExecutor(max_workers=max_workers)
+    futures = []
+    for lang in langs:
+        dataset_path = Path(os.path.join(dataset_dir, lang))
+        [
+            futures.append(executor.submit(deal_with_audio_dir, audio_dir))
+            for audio_dir in dataset_path.iterdir()
+            if audio_dir.is_dir()
+        ]
+    for futures in tqdm(futures, total=len(futures)):
+        sub_result, durations, vocab_set, bad_case_zh, bad_case_en = futures.result()
+        result.extend(sub_result)
+        duration_list.extend(durations)
+        text_vocab_set.update(vocab_set)
+        total_bad_case_zh += bad_case_zh
+        total_bad_case_en += bad_case_en
+    executor.shutdown()
+
+    # save preprocessed dataset to disk
+    if not os.path.exists(f"{save_dir}"):
+        os.makedirs(f"{save_dir}")
+    print(f"\nSaving to {save_dir} ...")
+
+    # dataset = Dataset.from_dict({"audio_path": audio_path_list, "text": text_list, "duration": duration_list})  # oom
+    # dataset.save_to_disk(f"{save_dir}/raw", max_shard_size="2GB")
+    with ArrowWriter(path=f"{save_dir}/raw.arrow") as writer:
+        for line in tqdm(result, desc="Writing to raw.arrow ..."):
+            writer.write(line)
+
+    # dup a json separately saving duration in case for DynamicBatchSampler ease
+    with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    # vocab map, i.e. tokenizer
+    # add alphabets and symbols (optional, if plan to ft on de/fr etc.)
+    # if tokenizer == "pinyin":
+    #     text_vocab_set.update([chr(i) for i in range(32, 127)] + [chr(i) for i in range(192, 256)])
+    with open(f"{save_dir}/vocab.txt", "w") as f:
+        for vocab in sorted(text_vocab_set):
+            f.write(vocab + "\n")
+
+    print(f"\nFor {dataset_name}, sample count: {len(result)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}")
+    print(f"For {dataset_name}, total {sum(duration_list)/3600:.2f} hours")
+    if "ZH" in langs:
+        print(f"Bad zh transcription case: {total_bad_case_zh}")
+    if "EN" in langs:
+        print(f"Bad en transcription case: {total_bad_case_en}\n")
+
+
+if __name__ == "__main__":
+    max_workers = 32
+
+    tokenizer = "pinyin"  # "pinyin" | "char"
+    polyphone = True
+
+    langs = ["ZH", "EN"]
+    dataset_dir = "<SOME_PATH>/Emilia_Dataset/raw"
+    dataset_name = f"Emilia_{'_'.join(langs)}_{tokenizer}"
+    save_dir = str(files("f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
+    print(f"\nPrepare for {dataset_name}, will save to {save_dir}\n")
+
+    main()
+
+    # Emilia               ZH & EN
+    # samples count       37837916   (after removal)
+    # pinyin vocab size       2543   (polyphone)
+    # total duration      95281.87   (hours)
+    # bad zh asr cnt        230435   (samples)
+    # bad eh asr cnt         37217   (samples)
+
+    # vocab size may be slightly different due to jieba tokenizer and pypinyin (e.g. way of polyphoneme)
+    # please be careful if using pretrained model, make sure the vocab.txt is same

src/f5_tts/train/datasets/prepare_wenetspeech4tts.py

@@ -0,0 +1,125 @@
+# generate audio text map for WenetSpeech4TTS
+# evaluate for vocab size
+
+import os
+import sys
+
+sys.path.append(os.getcwd())
+
+import json
+from concurrent.futures import ProcessPoolExecutor
+from importlib.resources import files
+from tqdm import tqdm
+
+import torchaudio
+from datasets import Dataset
+
+from f5_tts.model.utils import convert_char_to_pinyin
+
+
+def deal_with_sub_path_files(dataset_path, sub_path):
+    print(f"Dealing with: {sub_path}")
+
+    text_dir = os.path.join(dataset_path, sub_path, "txts")
+    audio_dir = os.path.join(dataset_path, sub_path, "wavs")
+    text_files = os.listdir(text_dir)
+
+    audio_paths, texts, durations = [], [], []
+    for text_file in tqdm(text_files):
+        with open(os.path.join(text_dir, text_file), "r", encoding="utf-8") as file:
+            first_line = file.readline().split("\t")
+        audio_nm = first_line[0]
+        audio_path = os.path.join(audio_dir, audio_nm + ".wav")
+        text = first_line[1].strip()
+
+        audio_paths.append(audio_path)
+
+        if tokenizer == "pinyin":
+            texts.extend(convert_char_to_pinyin([text], polyphone=polyphone))
+        elif tokenizer == "char":
+            texts.append(text)
+
+        audio, sample_rate = torchaudio.load(audio_path)
+        durations.append(audio.shape[-1] / sample_rate)
+
+    return audio_paths, texts, durations
+
+
+def main():
+    assert tokenizer in ["pinyin", "char"]
+
+    audio_path_list, text_list, duration_list = [], [], []
+
+    executor = ProcessPoolExecutor(max_workers=max_workers)
+    futures = []
+    for dataset_path in dataset_paths:
+        sub_items = os.listdir(dataset_path)
+        sub_paths = [item for item in sub_items if os.path.isdir(os.path.join(dataset_path, item))]
+        for sub_path in sub_paths:
+            futures.append(executor.submit(deal_with_sub_path_files, dataset_path, sub_path))
+    for future in tqdm(futures, total=len(futures)):
+        audio_paths, texts, durations = future.result()
+        audio_path_list.extend(audio_paths)
+        text_list.extend(texts)
+        duration_list.extend(durations)
+    executor.shutdown()
+
+    if not os.path.exists("data"):
+        os.makedirs("data")
+
+    print(f"\nSaving to {save_dir} ...")
+    dataset = Dataset.from_dict({"audio_path": audio_path_list, "text": text_list, "duration": duration_list})
+    dataset.save_to_disk(f"{save_dir}/raw", max_shard_size="2GB")  # arrow format
+
+    with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
+        json.dump(
+            {"duration": duration_list}, f, ensure_ascii=False
+        )  # dup a json separately saving duration in case for DynamicBatchSampler ease
+
+    print("\nEvaluating vocab size (all characters and symbols / all phonemes) ...")
+    text_vocab_set = set()
+    for text in tqdm(text_list):
+        text_vocab_set.update(list(text))
+
+    # add alphabets and symbols (optional, if plan to ft on de/fr etc.)
+    if tokenizer == "pinyin":
+        text_vocab_set.update([chr(i) for i in range(32, 127)] + [chr(i) for i in range(192, 256)])
+
+    with open(f"{save_dir}/vocab.txt", "w") as f:
+        for vocab in sorted(text_vocab_set):
+            f.write(vocab + "\n")
+    print(f"\nFor {dataset_name}, sample count: {len(text_list)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}\n")
+
+
+if __name__ == "__main__":
+    max_workers = 32
+
+    tokenizer = "pinyin"  # "pinyin" | "char"
+    polyphone = True
+    dataset_choice = 1  # 1: Premium, 2: Standard, 3: Basic
+
+    dataset_name = (
+        ["WenetSpeech4TTS_Premium", "WenetSpeech4TTS_Standard", "WenetSpeech4TTS_Basic"][dataset_choice - 1]
+        + "_"
+        + tokenizer
+    )
+    dataset_paths = [
+        "<SOME_PATH>/WenetSpeech4TTS/Basic",
+        "<SOME_PATH>/WenetSpeech4TTS/Standard",
+        "<SOME_PATH>/WenetSpeech4TTS/Premium",
+    ][-dataset_choice:]
+    save_dir = str(files("f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
+    print(f"\nChoose Dataset: {dataset_name}, will save to {save_dir}\n")
+
+    main()
+
+    # Results (if adding alphabets with accents and symbols):
+    # WenetSpeech4TTS       Basic     Standard     Premium
+    # samples count       3932473      1941220      407494
+    # pinyin vocab size      1349         1348        1344   (no polyphone)
+    #                           -            -        1459   (polyphone)
+    # char   vocab size      5264         5219        5042
+
+    # vocab size may be slightly different due to jieba tokenizer and pypinyin (e.g. way of polyphoneme)
+    # please be careful if using pretrained model, make sure the vocab.txt is same

src/f5_tts/train/finetune_cli.py

@@ -0,0 +1,145 @@
+import argparse
+import os
+import shutil
+
+from cached_path import cached_path
+from f5_tts.model import CFM, UNetT, DiT, Trainer
+from f5_tts.model.utils import get_tokenizer
+from f5_tts.model.dataset import load_dataset
+
+
+# -------------------------- Dataset Settings --------------------------- #
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+
+
+# -------------------------- Argument Parsing --------------------------- #
+def parse_args():
+    # batch_size_per_gpu = 1000 settting for gpu 8GB
+    # batch_size_per_gpu = 1600 settting for gpu 12GB
+    # batch_size_per_gpu = 2000 settting for gpu 16GB
+    # batch_size_per_gpu = 3200 settting for gpu 24GB
+
+    # num_warmup_updates = 300 for 5000 sample about 10 hours
+
+    # change save_per_updates , last_per_steps change this value what you need  ,
+
+    parser = argparse.ArgumentParser(description="Train CFM Model")
+
+    parser.add_argument(
+        "--exp_name", type=str, default="F5TTS_Base", choices=["F5TTS_Base", "E2TTS_Base"], help="Experiment name"
+    )
+    parser.add_argument("--dataset_name", type=str, default="Emilia_ZH_EN", help="Name of the dataset to use")
+    parser.add_argument("--learning_rate", type=float, default=1e-5, help="Learning rate for training")
+    parser.add_argument("--batch_size_per_gpu", type=int, default=3200, help="Batch size per GPU")
+    parser.add_argument(
+        "--batch_size_type", type=str, default="frame", choices=["frame", "sample"], help="Batch size type"
+    )
+    parser.add_argument("--max_samples", type=int, default=64, help="Max sequences per batch")
+    parser.add_argument("--grad_accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
+    parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping")
+    parser.add_argument("--epochs", type=int, default=10, help="Number of training epochs")
+    parser.add_argument("--num_warmup_updates", type=int, default=300, help="Warmup steps")
+    parser.add_argument("--save_per_updates", type=int, default=10000, help="Save checkpoint every X steps")
+    parser.add_argument("--last_per_steps", type=int, default=50000, help="Save last checkpoint every X steps")
+    parser.add_argument("--finetune", type=bool, default=True, help="Use Finetune")
+    parser.add_argument("--pretrain", type=str, default=None, help="Use pretrain model for finetune")
+    parser.add_argument(
+        "--tokenizer", type=str, default="pinyin", choices=["pinyin", "char", "custom"], help="Tokenizer type"
+    )
+    parser.add_argument(
+        "--tokenizer_path",
+        type=str,
+        default=None,
+        help="Path to custom tokenizer vocab file (only used if tokenizer = 'custom')",
+    )
+
+    return parser.parse_args()
+
+
+# -------------------------- Training Settings -------------------------- #
+
+
+def main():
+    args = parse_args()
+
+    # Model parameters based on experiment name
+    if args.exp_name == "F5TTS_Base":
+        wandb_resume_id = None
+        model_cls = DiT
+        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+        if args.finetune:
+            if args.pretrain is None:
+                ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt"))
+            else:
+                ckpt_path = args.pretrain
+    elif args.exp_name == "E2TTS_Base":
+        wandb_resume_id = None
+        model_cls = UNetT
+        model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+        if args.finetune:
+            if args.pretrain is None:
+                ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt"))
+            else:
+                ckpt_path = args.pretrain
+
+    if args.finetune:
+        path_ckpt = os.path.join("ckpts", args.dataset_name)
+        if not os.path.isdir(path_ckpt):
+            os.makedirs(path_ckpt, exist_ok=True)
+            shutil.copy2(ckpt_path, os.path.join(path_ckpt, os.path.basename(ckpt_path)))
+
+    checkpoint_path = os.path.join("ckpts", args.dataset_name)
+
+    # Use the tokenizer and tokenizer_path provided in the command line arguments
+    tokenizer = args.tokenizer
+    if tokenizer == "custom":
+        if not args.tokenizer_path:
+            raise ValueError("Custom tokenizer selected, but no tokenizer_path provided.")
+        tokenizer_path = args.tokenizer_path
+    else:
+        tokenizer_path = args.dataset_name
+
+    vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
+
+    mel_spec_kwargs = dict(
+        target_sample_rate=target_sample_rate,
+        n_mel_channels=n_mel_channels,
+        hop_length=hop_length,
+    )
+
+    model = CFM(
+        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=mel_spec_kwargs,
+        vocab_char_map=vocab_char_map,
+    )
+
+    trainer = Trainer(
+        model,
+        args.epochs,
+        args.learning_rate,
+        num_warmup_updates=args.num_warmup_updates,
+        save_per_updates=args.save_per_updates,
+        checkpoint_path=checkpoint_path,
+        batch_size=args.batch_size_per_gpu,
+        batch_size_type=args.batch_size_type,
+        max_samples=args.max_samples,
+        grad_accumulation_steps=args.grad_accumulation_steps,
+        max_grad_norm=args.max_grad_norm,
+        wandb_project=args.dataset_name,
+        wandb_run_name=args.exp_name,
+        wandb_resume_id=wandb_resume_id,
+        last_per_steps=args.last_per_steps,
+    )
+
+    train_dataset = load_dataset(args.dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)
+
+    trainer.train(
+        train_dataset,
+        resumable_with_seed=666,  # seed for shuffling dataset
+    )
+
+
+if __name__ == "__main__":
+    main()

src/f5_tts/train/finetune_gradio.py

@@ -0,0 +1,1223 @@
+import gc
+import json
+import os
+import platform
+import psutil
+import random
+import signal
+import shutil
+import subprocess
+import sys
+import tempfile
+import time
+from glob import glob
+
+import click
+import gradio as gr
+import librosa
+import numpy as np
+import torch
+import torchaudio
+from datasets import Dataset as Dataset_
+from datasets.arrow_writer import ArrowWriter
+from safetensors.torch import save_file
+from scipy.io import wavfile
+from transformers import pipeline
+
+from f5_tts.api import F5TTS
+from f5_tts.model.utils import convert_char_to_pinyin
+
+
+training_process = None
+system = platform.system()
+python_executable = sys.executable or "python"
+tts_api = None
+last_checkpoint = ""
+last_device = ""
+
+path_data = "data"
+
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+pipe = None
+
+
+# Load metadata
+def get_audio_duration(audio_path):
+    """Calculate the duration of an audio file."""
+    audio, sample_rate = torchaudio.load(audio_path)
+    num_channels = audio.shape[0]
+    return audio.shape[1] / (sample_rate * num_channels)
+
+
+def clear_text(text):
+    """Clean and prepare text by lowering the case and stripping whitespace."""
+    return text.lower().strip()
+
+
+def get_rms(
+    y,
+    frame_length=2048,
+    hop_length=512,
+    pad_mode="constant",
+):  # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
+    padding = (int(frame_length // 2), int(frame_length // 2))
+    y = np.pad(y, padding, mode=pad_mode)
+
+    axis = -1
+    # put our new within-frame axis at the end for now
+    out_strides = y.strides + tuple([y.strides[axis]])
+    # Reduce the shape on the framing axis
+    x_shape_trimmed = list(y.shape)
+    x_shape_trimmed[axis] -= frame_length - 1
+    out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
+    xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
+    if axis < 0:
+        target_axis = axis - 1
+    else:
+        target_axis = axis + 1
+    xw = np.moveaxis(xw, -1, target_axis)
+    # Downsample along the target axis
+    slices = [slice(None)] * xw.ndim
+    slices[axis] = slice(0, None, hop_length)
+    x = xw[tuple(slices)]
+
+    # Calculate power
+    power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
+
+    return np.sqrt(power)
+
+
+class Slicer:  # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
+    def __init__(
+        self,
+        sr: int,
+        threshold: float = -40.0,
+        min_length: int = 2000,
+        min_interval: int = 300,
+        hop_size: int = 20,
+        max_sil_kept: int = 2000,
+    ):
+        if not min_length >= min_interval >= hop_size:
+            raise ValueError("The following condition must be satisfied: min_length >= min_interval >= hop_size")
+        if not max_sil_kept >= hop_size:
+            raise ValueError("The following condition must be satisfied: max_sil_kept >= hop_size")
+        min_interval = sr * min_interval / 1000
+        self.threshold = 10 ** (threshold / 20.0)
+        self.hop_size = round(sr * hop_size / 1000)
+        self.win_size = min(round(min_interval), 4 * self.hop_size)
+        self.min_length = round(sr * min_length / 1000 / self.hop_size)
+        self.min_interval = round(min_interval / self.hop_size)
+        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
+
+    def _apply_slice(self, waveform, begin, end):
+        if len(waveform.shape) > 1:
+            return waveform[:, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)]
+        else:
+            return waveform[begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)]
+
+    # @timeit
+    def slice(self, waveform):
+        if len(waveform.shape) > 1:
+            samples = waveform.mean(axis=0)
+        else:
+            samples = waveform
+        if samples.shape[0] <= self.min_length:
+            return [waveform]
+        rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
+        sil_tags = []
+        silence_start = None
+        clip_start = 0
+        for i, rms in enumerate(rms_list):
+            # Keep looping while frame is silent.
+            if rms < self.threshold:
+                # Record start of silent frames.
+                if silence_start is None:
+                    silence_start = i
+                continue
+            # Keep looping while frame is not silent and silence start has not been recorded.
+            if silence_start is None:
+                continue
+            # Clear recorded silence start if interval is not enough or clip is too short
+            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
+            need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
+            if not is_leading_silence and not need_slice_middle:
+                silence_start = None
+                continue
+            # Need slicing. Record the range of silent frames to be removed.
+            if i - silence_start <= self.max_sil_kept:
+                pos = rms_list[silence_start : i + 1].argmin() + silence_start
+                if silence_start == 0:
+                    sil_tags.append((0, pos))
+                else:
+                    sil_tags.append((pos, pos))
+                clip_start = pos
+            elif i - silence_start <= self.max_sil_kept * 2:
+                pos = rms_list[i - self.max_sil_kept : silence_start + self.max_sil_kept + 1].argmin()
+                pos += i - self.max_sil_kept
+                pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
+                pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                    clip_start = pos_r
+                else:
+                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
+                    clip_start = max(pos_r, pos)
+            else:
+                pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start
+                pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept
+                if silence_start == 0:
+                    sil_tags.append((0, pos_r))
+                else:
+                    sil_tags.append((pos_l, pos_r))
+                clip_start = pos_r
+            silence_start = None
+        # Deal with trailing silence.
+        total_frames = rms_list.shape[0]
+        if silence_start is not None and total_frames - silence_start >= self.min_interval:
+            silence_end = min(total_frames, silence_start + self.max_sil_kept)
+            pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
+            sil_tags.append((pos, total_frames + 1))
+        # Apply and return slices.
+        ####音频+起始时间+终止时间
+        if len(sil_tags) == 0:
+            return [[waveform, 0, int(total_frames * self.hop_size)]]
+        else:
+            chunks = []
+            if sil_tags[0][0] > 0:
+                chunks.append([self._apply_slice(waveform, 0, sil_tags[0][0]), 0, int(sil_tags[0][0] * self.hop_size)])
+            for i in range(len(sil_tags) - 1):
+                chunks.append(
+                    [
+                        self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]),
+                        int(sil_tags[i][1] * self.hop_size),
+                        int(sil_tags[i + 1][0] * self.hop_size),
+                    ]
+                )
+            if sil_tags[-1][1] < total_frames:
+                chunks.append(
+                    [
+                        self._apply_slice(waveform, sil_tags[-1][1], total_frames),
+                        int(sil_tags[-1][1] * self.hop_size),
+                        int(total_frames * self.hop_size),
+                    ]
+                )
+            return chunks
+
+
+# terminal
+def terminate_process_tree(pid, including_parent=True):
+    try:
+        parent = psutil.Process(pid)
+    except psutil.NoSuchProcess:
+        # Process already terminated
+        return
+
+    children = parent.children(recursive=True)
+    for child in children:
+        try:
+            os.kill(child.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+    if including_parent:
+        try:
+            os.kill(parent.pid, signal.SIGTERM)  # or signal.SIGKILL
+        except OSError:
+            pass
+
+
+def terminate_process(pid):
+    if system == "Windows":
+        cmd = f"taskkill /t /f /pid {pid}"
+        os.system(cmd)
+    else:
+        terminate_process_tree(pid)
+
+
+def start_training(
+    dataset_name="",
+    exp_name="F5TTS_Base",
+    learning_rate=1e-4,
+    batch_size_per_gpu=400,
+    batch_size_type="frame",
+    max_samples=64,
+    grad_accumulation_steps=1,
+    max_grad_norm=1.0,
+    epochs=11,
+    num_warmup_updates=200,
+    save_per_updates=400,
+    last_per_steps=800,
+    finetune=True,
+    file_checkpoint_train="",
+    tokenizer_type="pinyin",
+    tokenizer_file="",
+    mixed_precision="fp16",
+):
+    global training_process, tts_api
+
+    if tts_api is not None:
+        del tts_api
+        gc.collect()
+        torch.cuda.empty_cache()
+        tts_api = None
+
+    path_project = os.path.join(path_data, dataset_name)
+
+    if not os.path.isdir(path_project):
+        yield (
+            f"There is not project with name {dataset_name}",
+            gr.update(interactive=True),
+            gr.update(interactive=False),
+        )
+        return
+
+    file_raw = os.path.join(path_project, "raw.arrow")
+    if not os.path.isfile(file_raw):
+        yield f"There is no file {file_raw}", gr.update(interactive=True), gr.update(interactive=False)
+        return
+
+    # Check if a training process is already running
+    if training_process is not None:
+        return "Train run already!", gr.update(interactive=False), gr.update(interactive=True)
+
+    yield "start train", gr.update(interactive=False), gr.update(interactive=False)
+
+    # Command to run the training script with the specified arguments
+
+    if tokenizer_file == "":
+        if dataset_name.endswith("_pinyin"):
+            tokenizer_type = "pinyin"
+        elif dataset_name.endswith("_char"):
+            tokenizer_type = "char"
+    else:
+        tokenizer_file = "custom"
+
+    dataset_name = dataset_name.replace("_pinyin", "").replace("_char", "")
+
+    if mixed_precision != "none":
+        fp16 = f"--mixed_precision={mixed_precision}"
+    else:
+        fp16 = ""
+
+    cmd = (
+        f"accelerate launch {fp16} finetune-cli.py --exp_name {exp_name} "
+        f"--learning_rate {learning_rate} "
+        f"--batch_size_per_gpu {batch_size_per_gpu} "
+        f"--batch_size_type {batch_size_type} "
+        f"--max_samples {max_samples} "
+        f"--grad_accumulation_steps {grad_accumulation_steps} "
+        f"--max_grad_norm {max_grad_norm} "
+        f"--epochs {epochs} "
+        f"--num_warmup_updates {num_warmup_updates} "
+        f"--save_per_updates {save_per_updates} "
+        f"--last_per_steps {last_per_steps} "
+        f"--dataset_name {dataset_name}"
+    )
+    if finetune:
+        cmd += f" --finetune {finetune}"
+
+    if file_checkpoint_train != "":
+        cmd += f" --file_checkpoint_train {file_checkpoint_train}"
+
+    if tokenizer_file != "":
+        cmd += f" --tokenizer_path {tokenizer_file}"
+
+    cmd += f" --tokenizer {tokenizer_type} "
+
+    print(cmd)
+
+    try:
+        # Start the training process
+        training_process = subprocess.Popen(cmd, shell=True)
+
+        time.sleep(5)
+        yield "train start", gr.update(interactive=False), gr.update(interactive=True)
+
+        # Wait for the training process to finish
+        training_process.wait()
+        time.sleep(1)
+
+        if training_process is None:
+            text_info = "train stop"
+        else:
+            text_info = "train complete !"
+
+    except Exception as e:  # Catch all exceptions
+        # Ensure that we reset the training process variable in case of an error
+        text_info = f"An error occurred: {str(e)}"
+
+    training_process = None
+
+    yield text_info, gr.update(interactive=True), gr.update(interactive=False)
+
+
+def stop_training():
+    global training_process
+    if training_process is None:
+        return "Train not run !", gr.update(interactive=True), gr.update(interactive=False)
+    terminate_process_tree(training_process.pid)
+    training_process = None
+    return "train stop", gr.update(interactive=True), gr.update(interactive=False)
+
+
+def get_list_projects():
+    project_list = []
+    for folder in os.listdir("data"):
+        path_folder = os.path.join("data", folder)
+        if not os.path.isdir(path_folder):
+            continue
+        folder = folder.lower()
+        if folder == "emilia_zh_en_pinyin":
+            continue
+        project_list.append(folder)
+
+    projects_selelect = None if not project_list else project_list[-1]
+
+    return project_list, projects_selelect
+
+
+def create_data_project(name, tokenizer_type):
+    name += "_" + tokenizer_type
+    os.makedirs(os.path.join(path_data, name), exist_ok=True)
+    os.makedirs(os.path.join(path_data, name, "dataset"), exist_ok=True)
+    project_list, projects_selelect = get_list_projects()
+    return gr.update(choices=project_list, value=name)
+
+
+def transcribe(file_audio, language="english"):
+    global pipe
+
+    if pipe is None:
+        pipe = pipeline(
+            "automatic-speech-recognition",
+            model="openai/whisper-large-v3-turbo",
+            torch_dtype=torch.float16,
+            device=device,
+        )
+
+    text_transcribe = pipe(
+        file_audio,
+        chunk_length_s=30,
+        batch_size=128,
+        generate_kwargs={"task": "transcribe", "language": language},
+        return_timestamps=False,
+    )["text"].strip()
+    return text_transcribe
+
+
+def transcribe_all(name_project, audio_files, language, user=False, progress=gr.Progress()):
+    path_project = os.path.join(path_data, name_project)
+    path_dataset = os.path.join(path_project, "dataset")
+    path_project_wavs = os.path.join(path_project, "wavs")
+    file_metadata = os.path.join(path_project, "metadata.csv")
+
+    if not user:
+        if audio_files is None:
+            return "You need to load an audio file."
+
+    if os.path.isdir(path_project_wavs):
+        shutil.rmtree(path_project_wavs)
+
+    if os.path.isfile(file_metadata):
+        os.remove(file_metadata)
+
+    os.makedirs(path_project_wavs, exist_ok=True)
+
+    if user:
+        file_audios = [
+            file
+            for format in ("*.wav", "*.ogg", "*.opus", "*.mp3", "*.flac")
+            for file in glob(os.path.join(path_dataset, format))
+        ]
+        if file_audios == []:
+            return "No audio file was found in the dataset."
+    else:
+        file_audios = audio_files
+
+    alpha = 0.5
+    _max = 1.0
+    slicer = Slicer(24000)
+
+    num = 0
+    error_num = 0
+    data = ""
+    for file_audio in progress.tqdm(file_audios, desc="transcribe files", total=len((file_audios))):
+        audio, _ = librosa.load(file_audio, sr=24000, mono=True)
+
+        list_slicer = slicer.slice(audio)
+        for chunk, start, end in progress.tqdm(list_slicer, total=len(list_slicer), desc="slicer files"):
+            name_segment = os.path.join(f"segment_{num}")
+            file_segment = os.path.join(path_project_wavs, f"{name_segment}.wav")
+
+            tmp_max = np.abs(chunk).max()
+            if tmp_max > 1:
+                chunk /= tmp_max
+            chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk
+            wavfile.write(file_segment, 24000, (chunk * 32767).astype(np.int16))
+
+            try:
+                text = transcribe(file_segment, language)
+                text = text.lower().strip().replace('"', "")
+
+                data += f"{name_segment}|{text}\n"
+
+                num += 1
+            except:  # noqa: E722
+                error_num += 1
+
+    with open(file_metadata, "w", encoding="utf-8-sig") as f:
+        f.write(data)
+
+    if error_num != []:
+        error_text = f"\nerror files : {error_num}"
+    else:
+        error_text = ""
+
+    return f"transcribe complete samples : {num}\npath : {path_project_wavs}{error_text}"
+
+
+def format_seconds_to_hms(seconds):
+    hours = int(seconds / 3600)
+    minutes = int((seconds % 3600) / 60)
+    seconds = seconds % 60
+    return "{:02d}:{:02d}:{:02d}".format(hours, minutes, int(seconds))
+
+
+def create_metadata(name_project, ch_tokenizer, progress=gr.Progress()):
+    path_project = os.path.join(path_data, name_project)
+    path_project_wavs = os.path.join(path_project, "wavs")
+    file_metadata = os.path.join(path_project, "metadata.csv")
+    file_raw = os.path.join(path_project, "raw.arrow")
+    file_duration = os.path.join(path_project, "duration.json")
+    file_vocab = os.path.join(path_project, "vocab.txt")
+
+    if not os.path.isfile(file_metadata):
+        return "The file was not found in " + file_metadata, ""
+
+    with open(file_metadata, "r", encoding="utf-8-sig") as f:
+        data = f.read()
+
+    audio_path_list = []
+    text_list = []
+    duration_list = []
+
+    count = data.split("\n")
+    lenght = 0
+    result = []
+    error_files = []
+    text_vocab_set = set()
+    for line in progress.tqdm(data.split("\n"), total=count):
+        sp_line = line.split("|")
+        if len(sp_line) != 2:
+            continue
+        name_audio, text = sp_line[:2]
+
+        file_audio = os.path.join(path_project_wavs, name_audio + ".wav")
+
+        if not os.path.isfile(file_audio):
+            error_files.append([file_audio, "error path"])
+            continue
+
+        try:
+            duration = get_audio_duration(file_audio)
+        except Exception as e:
+            error_files.append([file_audio, "duration"])
+            print(f"Error processing {file_audio}: {e}")
+            continue
+
+        if duration < 1 and duration > 25:
+            error_files.append([file_audio, "duration < 1 and > 25 "])
+            continue
+        if len(text) < 4:
+            error_files.append([file_audio, "very small text len 3"])
+            continue
+
+        text = clear_text(text)
+        text = convert_char_to_pinyin([text], polyphone=True)[0]
+
+        audio_path_list.append(file_audio)
+        duration_list.append(duration)
+        text_list.append(text)
+
+        result.append({"audio_path": file_audio, "text": text, "duration": duration})
+        if ch_tokenizer:
+            text_vocab_set.update(list(text))
+
+        lenght += duration
+
+    if duration_list == []:
+        return f"Error: No audio files found in the specified path : {path_project_wavs}", ""
+
+    min_second = round(min(duration_list), 2)
+    max_second = round(max(duration_list), 2)
+
+    with ArrowWriter(path=file_raw, writer_batch_size=1) as writer:
+        for line in progress.tqdm(result, total=len(result), desc="prepare data"):
+            writer.write(line)
+
+    with open(file_duration, "w") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    new_vocal = ""
+    if not ch_tokenizer:
+        file_vocab_finetune = "data/Emilia_ZH_EN_pinyin/vocab.txt"
+        if not os.path.isfile(file_vocab_finetune):
+            return "Error: Vocabulary file 'Emilia_ZH_EN_pinyin' not found!"
+        shutil.copy2(file_vocab_finetune, file_vocab)
+
+        with open(file_vocab, "r", encoding="utf-8-sig") as f:
+            vocab_char_map = {}
+            for i, char in enumerate(f):
+                vocab_char_map[char[:-1]] = i
+        vocab_size = len(vocab_char_map)
+
+    else:
+        with open(file_vocab, "w", encoding="utf-8-sig") as f:
+            for vocab in sorted(text_vocab_set):
+                f.write(vocab + "\n")
+                new_vocal += vocab + "\n"
+        vocab_size = len(text_vocab_set)
+
+    if error_files != []:
+        error_text = "\n".join([" = ".join(item) for item in error_files])
+    else:
+        error_text = ""
+
+    return (
+        f"prepare complete \nsamples : {len(text_list)}\ntime data : {format_seconds_to_hms(lenght)}\nmin sec : {min_second}\nmax sec : {max_second}\nfile_arrow : {file_raw}\nvocab : {vocab_size}\n{error_text}",
+        new_vocal,
+    )
+
+
+def check_user(value):
+    return gr.update(visible=not value), gr.update(visible=value)
+
+
+def calculate_train(
+    name_project,
+    batch_size_type,
+    max_samples,
+    learning_rate,
+    num_warmup_updates,
+    save_per_updates,
+    last_per_steps,
+    finetune,
+):
+    path_project = os.path.join(path_data, name_project)
+    file_duraction = os.path.join(path_project, "duration.json")
+
+    if not os.path.isfile(file_duraction):
+        return (
+            1000,
+            max_samples,
+            num_warmup_updates,
+            save_per_updates,
+            last_per_steps,
+            "project not found !",
+            learning_rate,
+        )
+
+    with open(file_duraction, "r") as file:
+        data = json.load(file)
+
+    duration_list = data["duration"]
+    samples = len(duration_list)
+    hours = sum(duration_list) / 3600
+
+    # if torch.cuda.is_available():
+    # gpu_properties = torch.cuda.get_device_properties(0)
+    # total_memory = gpu_properties.total_memory / (1024**3)
+    # elif torch.backends.mps.is_available():
+    # total_memory = psutil.virtual_memory().available / (1024**3)
+
+    if torch.cuda.is_available():
+        gpu_count = torch.cuda.device_count()
+        total_memory = 0
+        for i in range(gpu_count):
+            gpu_properties = torch.cuda.get_device_properties(i)
+            total_memory += gpu_properties.total_memory / (1024**3)  # in GB
+
+    elif torch.backends.mps.is_available():
+        gpu_count = 1
+        total_memory = psutil.virtual_memory().available / (1024**3)
+
+    if batch_size_type == "frame":
+        batch = int(total_memory * 0.5)
+        batch = (lambda num: num + 1 if num % 2 != 0 else num)(batch)
+        batch_size_per_gpu = int(38400 / batch)
+    else:
+        batch_size_per_gpu = int(total_memory / 8)
+        batch_size_per_gpu = (lambda num: num + 1 if num % 2 != 0 else num)(batch_size_per_gpu)
+        batch = batch_size_per_gpu
+
+    if batch_size_per_gpu <= 0:
+        batch_size_per_gpu = 1
+
+    if samples < 64:
+        max_samples = int(samples * 0.25)
+    else:
+        max_samples = 64
+
+    num_warmup_updates = int(samples * 0.05)
+    save_per_updates = int(samples * 0.10)
+    last_per_steps = int(save_per_updates * 5)
+
+    max_samples = (lambda num: num + 1 if num % 2 != 0 else num)(max_samples)
+    num_warmup_updates = (lambda num: num + 1 if num % 2 != 0 else num)(num_warmup_updates)
+    save_per_updates = (lambda num: num + 1 if num % 2 != 0 else num)(save_per_updates)
+    last_per_steps = (lambda num: num + 1 if num % 2 != 0 else num)(last_per_steps)
+
+    total_hours = hours
+    mel_hop_length = 256
+    mel_sampling_rate = 24000
+
+    # target
+    wanted_max_updates = 1000000
+
+    # train params
+    gpus = gpu_count
+    frames_per_gpu = batch_size_per_gpu  # 8 * 38400 = 307200
+    grad_accum = 1
+
+    # intermediate
+    mini_batch_frames = frames_per_gpu * grad_accum * gpus
+    mini_batch_hours = mini_batch_frames * mel_hop_length / mel_sampling_rate / 3600
+    updates_per_epoch = total_hours / mini_batch_hours
+    # steps_per_epoch = updates_per_epoch * grad_accum
+    epochs = wanted_max_updates / updates_per_epoch
+
+    if finetune:
+        learning_rate = 1e-5
+    else:
+        learning_rate = 7.5e-5
+
+    return (
+        batch_size_per_gpu,
+        max_samples,
+        num_warmup_updates,
+        save_per_updates,
+        last_per_steps,
+        samples,
+        learning_rate,
+        int(epochs),
+    )
+
+
+def extract_and_save_ema_model(checkpoint_path: str, new_checkpoint_path: str, safetensors: bool) -> str:
+    try:
+        checkpoint = torch.load(checkpoint_path)
+        print("Original Checkpoint Keys:", checkpoint.keys())
+
+        ema_model_state_dict = checkpoint.get("ema_model_state_dict", None)
+        if ema_model_state_dict is None:
+            return "No 'ema_model_state_dict' found in the checkpoint."
+
+        if safetensors:
+            new_checkpoint_path = new_checkpoint_path.replace(".pt", ".safetensors")
+            save_file(ema_model_state_dict, new_checkpoint_path)
+        else:
+            new_checkpoint_path = new_checkpoint_path.replace(".safetensors", ".pt")
+            new_checkpoint = {"ema_model_state_dict": ema_model_state_dict}
+            torch.save(new_checkpoint, new_checkpoint_path)
+
+        return f"New checkpoint saved at: {new_checkpoint_path}"
+
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+
+def vocab_check(project_name):
+    name_project = project_name
+    path_project = os.path.join(path_data, name_project)
+
+    file_metadata = os.path.join(path_project, "metadata.csv")
+
+    file_vocab = "data/Emilia_ZH_EN_pinyin/vocab.txt"
+    if not os.path.isfile(file_vocab):
+        return f"the file {file_vocab} not found !"
+
+    with open(file_vocab, "r", encoding="utf-8-sig") as f:
+        data = f.read()
+        vocab = data.split("\n")
+        vocab = set(vocab)
+
+    if not os.path.isfile(file_metadata):
+        return f"the file {file_metadata} not found !"
+
+    with open(file_metadata, "r", encoding="utf-8-sig") as f:
+        data = f.read()
+
+    miss_symbols = []
+    miss_symbols_keep = {}
+    for item in data.split("\n"):
+        sp = item.split("|")
+        if len(sp) != 2:
+            continue
+
+        text = sp[1].lower().strip()
+
+        for t in text:
+            if t not in vocab and t not in miss_symbols_keep:
+                miss_symbols.append(t)
+                miss_symbols_keep[t] = t
+    if miss_symbols == []:
+        info = "You can train using your language !"
+    else:
+        info = f"The following symbols are missing in your language : {len(miss_symbols)}\n\n" + "\n".join(miss_symbols)
+
+    return info
+
+
+def get_random_sample_prepare(project_name):
+    name_project = project_name
+    path_project = os.path.join(path_data, name_project)
+    file_arrow = os.path.join(path_project, "raw.arrow")
+    if not os.path.isfile(file_arrow):
+        return "", None
+    dataset = Dataset_.from_file(file_arrow)
+    random_sample = dataset.shuffle(seed=random.randint(0, 1000)).select([0])
+    text = "[" + " , ".join(["' " + t + " '" for t in random_sample["text"][0]]) + "]"
+    audio_path = random_sample["audio_path"][0]
+    return text, audio_path
+
+
+def get_random_sample_transcribe(project_name):
+    name_project = project_name
+    path_project = os.path.join(path_data, name_project)
+    file_metadata = os.path.join(path_project, "metadata.csv")
+    if not os.path.isfile(file_metadata):
+        return "", None
+
+    data = ""
+    with open(file_metadata, "r", encoding="utf-8-sig") as f:
+        data = f.read()
+
+    list_data = []
+    for item in data.split("\n"):
+        sp = item.split("|")
+        if len(sp) != 2:
+            continue
+        list_data.append([os.path.join(path_project, "wavs", sp[0] + ".wav"), sp[1]])
+
+    if list_data == []:
+        return "", None
+
+    random_item = random.choice(list_data)
+
+    return random_item[1], random_item[0]
+
+
+def get_random_sample_infer(project_name):
+    text, audio = get_random_sample_transcribe(project_name)
+    return (
+        text,
+        text,
+        audio,
+    )
+
+
+def infer(file_checkpoint, exp_name, ref_text, ref_audio, gen_text, nfe_step):
+    global last_checkpoint, last_device, tts_api
+
+    if not os.path.isfile(file_checkpoint):
+        return None, "checkpoint not found!"
+
+    if training_process is not None:
+        device_test = "cpu"
+    else:
+        device_test = None
+
+    if last_checkpoint != file_checkpoint or last_device != device_test:
+        if last_checkpoint != file_checkpoint:
+            last_checkpoint = file_checkpoint
+        if last_device != device_test:
+            last_device = device_test
+
+        tts_api = F5TTS(model_type=exp_name, ckpt_file=file_checkpoint, device=device_test)
+
+        print("update", device_test, file_checkpoint)
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+        tts_api.infer(gen_text=gen_text, ref_text=ref_text, ref_file=ref_audio, nfe_step=nfe_step, file_wave=f.name)
+        return f.name, tts_api.device
+
+
+def check_finetune(finetune):
+    return gr.update(interactive=finetune), gr.update(interactive=finetune), gr.update(interactive=finetune)
+
+
+def get_checkpoints_project(project_name, is_gradio=True):
+    if project_name is None:
+        return [], ""
+    project_name = project_name.replace("_pinyin", "").replace("_char", "")
+    path_project_ckpts = os.path.join("ckpts", project_name)
+
+    if os.path.isdir(path_project_ckpts):
+        files_checkpoints = glob(os.path.join(path_project_ckpts, "*.pt"))
+        files_checkpoints = sorted(
+            files_checkpoints,
+            key=lambda x: int(os.path.basename(x).split("_")[1].split(".")[0])
+            if os.path.basename(x) != "model_last.pt"
+            else float("inf"),
+        )
+    else:
+        files_checkpoints = []
+
+    selelect_checkpoint = None if not files_checkpoints else files_checkpoints[0]
+
+    if is_gradio:
+        return gr.update(choices=files_checkpoints, value=selelect_checkpoint)
+
+    return files_checkpoints, selelect_checkpoint
+
+
+def get_gpu_stats():
+    gpu_stats = ""
+
+    if torch.cuda.is_available():
+        gpu_count = torch.cuda.device_count()
+        for i in range(gpu_count):
+            gpu_name = torch.cuda.get_device_name(i)
+            gpu_properties = torch.cuda.get_device_properties(i)
+            total_memory = gpu_properties.total_memory / (1024**3)  # in GB
+            allocated_memory = torch.cuda.memory_allocated(i) / (1024**2)  # in MB
+            reserved_memory = torch.cuda.memory_reserved(i) / (1024**2)  # in MB
+
+            gpu_stats += (
+                f"GPU {i} Name: {gpu_name}\n"
+                f"Total GPU memory (GPU {i}): {total_memory:.2f} GB\n"
+                f"Allocated GPU memory (GPU {i}): {allocated_memory:.2f} MB\n"
+                f"Reserved GPU memory (GPU {i}): {reserved_memory:.2f} MB\n\n"
+            )
+
+    elif torch.backends.mps.is_available():
+        gpu_count = 1
+        gpu_stats += "MPS GPU\n"
+        total_memory = psutil.virtual_memory().total / (
+            1024**3
+        )  # Total system memory (MPS doesn't have its own memory)
+        allocated_memory = 0
+        reserved_memory = 0
+
+        gpu_stats += (
+            f"Total system memory: {total_memory:.2f} GB\n"
+            f"Allocated GPU memory (MPS): {allocated_memory:.2f} MB\n"
+            f"Reserved GPU memory (MPS): {reserved_memory:.2f} MB\n"
+        )
+
+    else:
+        gpu_stats = "No GPU available"
+
+    return gpu_stats
+
+
+def get_cpu_stats():
+    cpu_usage = psutil.cpu_percent(interval=1)
+    memory_info = psutil.virtual_memory()
+    memory_used = memory_info.used / (1024**2)
+    memory_total = memory_info.total / (1024**2)
+    memory_percent = memory_info.percent
+
+    pid = os.getpid()
+    process = psutil.Process(pid)
+    nice_value = process.nice()
+
+    cpu_stats = (
+        f"CPU Usage: {cpu_usage:.2f}%\n"
+        f"System Memory: {memory_used:.2f} MB used / {memory_total:.2f} MB total ({memory_percent}% used)\n"
+        f"Process Priority (Nice value): {nice_value}"
+    )
+
+    return cpu_stats
+
+
+def get_combined_stats():
+    gpu_stats = get_gpu_stats()
+    cpu_stats = get_cpu_stats()
+    combined_stats = f"### GPU Stats\n{gpu_stats}\n\n### CPU Stats\n{cpu_stats}"
+    return combined_stats
+
+
+with gr.Blocks() as app:
+    gr.Markdown(
+        """
+# E2/F5 TTS AUTOMATIC FINETUNE 
+
+This is a local web UI for F5 TTS with advanced batch processing support. This app supports the following TTS models:
+
+* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching)
+* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS)
+
+The checkpoints support English and Chinese.
+
+for tutorial and updates check here (https://github.com/SWivid/F5-TTS/discussions/143)
+"""
+    )
+
+    with gr.Row():
+        projects, projects_selelect = get_list_projects()
+        tokenizer_type = gr.Radio(label="Tokenizer Type", choices=["pinyin", "char"], value="pinyin")
+        project_name = gr.Textbox(label="project name", value="my_speak")
+        bt_create = gr.Button("create new project")
+
+    cm_project = gr.Dropdown(choices=projects, value=projects_selelect, label="Project", allow_custom_value=True)
+
+    bt_create.click(fn=create_data_project, inputs=[project_name, tokenizer_type], outputs=[cm_project])
+
+    with gr.Tabs():
+        with gr.TabItem("transcribe Data"):
+            ch_manual = gr.Checkbox(label="audio from path", value=False)
+
+            mark_info_transcribe = gr.Markdown(
+                """```plaintext    
+     Place your 'wavs' folder and 'metadata.csv' file in the {your_project_name}' directory. 
+                 
+     my_speak/
+     │
+     └── dataset/
+         ├── audio1.wav
+         └── audio2.wav
+         ...
+     ```""",
+                visible=False,
+            )
+
+            audio_speaker = gr.File(label="voice", type="filepath", file_count="multiple")
+            txt_lang = gr.Text(label="Language", value="english")
+            bt_transcribe = bt_create = gr.Button("transcribe")
+            txt_info_transcribe = gr.Text(label="info", value="")
+            bt_transcribe.click(
+                fn=transcribe_all,
+                inputs=[cm_project, audio_speaker, txt_lang, ch_manual],
+                outputs=[txt_info_transcribe],
+            )
+            ch_manual.change(fn=check_user, inputs=[ch_manual], outputs=[audio_speaker, mark_info_transcribe])
+
+            random_sample_transcribe = gr.Button("random sample")
+
+            with gr.Row():
+                random_text_transcribe = gr.Text(label="Text")
+                random_audio_transcribe = gr.Audio(label="Audio", type="filepath")
+
+            random_sample_transcribe.click(
+                fn=get_random_sample_transcribe,
+                inputs=[cm_project],
+                outputs=[random_text_transcribe, random_audio_transcribe],
+            )
+
+        with gr.TabItem("prepare Data"):
+            gr.Markdown(
+                """```plaintext    
+     place all your wavs folder and your metadata.csv file in {your name project}                                 
+     my_speak/
+     │
+     ├── wavs/
+     │   ├── audio1.wav
+     │   └── audio2.wav
+     |   ...
+     │
+     └── metadata.csv
+      
+     file format metadata.csv
+
+     audio1|text1
+     audio2|text1
+     ...
+
+     ```"""
+            )
+            ch_tokenizern = gr.Checkbox(label="create vocabulary from dataset", value=False)
+            bt_prepare = bt_create = gr.Button("prepare")
+            txt_info_prepare = gr.Text(label="info", value="")
+            txt_vocab_prepare = gr.Text(label="vocab", value="")
+            bt_prepare.click(
+                fn=create_metadata, inputs=[cm_project, ch_tokenizern], outputs=[txt_info_prepare, txt_vocab_prepare]
+            )
+
+            random_sample_prepare = gr.Button("random sample")
+
+            with gr.Row():
+                random_text_prepare = gr.Text(label="Pinyin")
+                random_audio_prepare = gr.Audio(label="Audio", type="filepath")
+
+            random_sample_prepare.click(
+                fn=get_random_sample_prepare, inputs=[cm_project], outputs=[random_text_prepare, random_audio_prepare]
+            )
+
+        with gr.TabItem("train Data"):
+            with gr.Row():
+                bt_calculate = bt_create = gr.Button("Auto Settings")
+                lb_samples = gr.Label(label="samples")
+                batch_size_type = gr.Radio(label="Batch Size Type", choices=["frame", "sample"], value="frame")
+
+            with gr.Row():
+                ch_finetune = bt_create = gr.Checkbox(label="finetune", value=True)
+                tokenizer_file = gr.Textbox(label="Tokenizer File", value="")
+                file_checkpoint_train = gr.Textbox(label="Pretrain Model", value="")
+
+            with gr.Row():
+                exp_name = gr.Radio(label="Model", choices=["F5TTS_Base", "E2TTS_Base"], value="F5TTS_Base")
+                learning_rate = gr.Number(label="Learning Rate", value=1e-5, step=1e-5)
+
+            with gr.Row():
+                batch_size_per_gpu = gr.Number(label="Batch Size per GPU", value=1000)
+                max_samples = gr.Number(label="Max Samples", value=64)
+
+            with gr.Row():
+                grad_accumulation_steps = gr.Number(label="Gradient Accumulation Steps", value=1)
+                max_grad_norm = gr.Number(label="Max Gradient Norm", value=1.0)
+
+            with gr.Row():
+                epochs = gr.Number(label="Epochs", value=10)
+                num_warmup_updates = gr.Number(label="Warmup Updates", value=5)
+
+            with gr.Row():
+                save_per_updates = gr.Number(label="Save per Updates", value=10)
+                last_per_steps = gr.Number(label="Last per Steps", value=50)
+
+            with gr.Row():
+                mixed_precision = gr.Radio(label="mixed_precision", choices=["none", "fp16", "fpb16"], value="none")
+                start_button = gr.Button("Start Training")
+                stop_button = gr.Button("Stop Training", interactive=False)
+
+            txt_info_train = gr.Text(label="info", value="")
+            start_button.click(
+                fn=start_training,
+                inputs=[
+                    cm_project,
+                    exp_name,
+                    learning_rate,
+                    batch_size_per_gpu,
+                    batch_size_type,
+                    max_samples,
+                    grad_accumulation_steps,
+                    max_grad_norm,
+                    epochs,
+                    num_warmup_updates,
+                    save_per_updates,
+                    last_per_steps,
+                    ch_finetune,
+                    file_checkpoint_train,
+                    tokenizer_type,
+                    tokenizer_file,
+                    mixed_precision,
+                ],
+                outputs=[txt_info_train, start_button, stop_button],
+            )
+            stop_button.click(fn=stop_training, outputs=[txt_info_train, start_button, stop_button])
+
+            bt_calculate.click(
+                fn=calculate_train,
+                inputs=[
+                    cm_project,
+                    batch_size_type,
+                    max_samples,
+                    learning_rate,
+                    num_warmup_updates,
+                    save_per_updates,
+                    last_per_steps,
+                    ch_finetune,
+                ],
+                outputs=[
+                    batch_size_per_gpu,
+                    max_samples,
+                    num_warmup_updates,
+                    save_per_updates,
+                    last_per_steps,
+                    lb_samples,
+                    learning_rate,
+                    epochs,
+                ],
+            )
+
+            ch_finetune.change(
+                check_finetune, inputs=[ch_finetune], outputs=[file_checkpoint_train, tokenizer_file, tokenizer_type]
+            )
+
+        with gr.TabItem("reduse checkpoint"):
+            txt_path_checkpoint = gr.Text(label="path checkpoint :")
+            txt_path_checkpoint_small = gr.Text(label="path output :")
+            ch_safetensors = gr.Checkbox(label="safetensors", value="")
+            txt_info_reduse = gr.Text(label="info", value="")
+            reduse_button = gr.Button("reduse")
+            reduse_button.click(
+                fn=extract_and_save_ema_model,
+                inputs=[txt_path_checkpoint, txt_path_checkpoint_small, ch_safetensors],
+                outputs=[txt_info_reduse],
+            )
+
+        with gr.TabItem("vocab check"):
+            check_button = gr.Button("check vocab")
+            txt_info_check = gr.Text(label="info", value="")
+            check_button.click(fn=vocab_check, inputs=[cm_project], outputs=[txt_info_check])
+
+        with gr.TabItem("test model"):
+            exp_name = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS")
+            list_checkpoints, checkpoint_select = get_checkpoints_project(projects_selelect, False)
+
+            nfe_step = gr.Number(label="n_step", value=32)
+
+            with gr.Row():
+                cm_checkpoint = gr.Dropdown(
+                    choices=list_checkpoints, value=checkpoint_select, label="checkpoints", allow_custom_value=True
+                )
+                bt_checkpoint_refresh = gr.Button("refresh")
+
+            random_sample_infer = gr.Button("random sample")
+
+            ref_text = gr.Textbox(label="ref text")
+            ref_audio = gr.Audio(label="audio ref", type="filepath")
+            gen_text = gr.Textbox(label="gen text")
+            random_sample_infer.click(
+                fn=get_random_sample_infer, inputs=[cm_project], outputs=[ref_text, gen_text, ref_audio]
+            )
+
+            with gr.Row():
+                txt_info_gpu = gr.Textbox("", label="device")
+                check_button_infer = gr.Button("infer")
+
+            gen_audio = gr.Audio(label="audio gen", type="filepath")
+
+            check_button_infer.click(
+                fn=infer,
+                inputs=[cm_checkpoint, exp_name, ref_text, ref_audio, gen_text, nfe_step],
+                outputs=[gen_audio, txt_info_gpu],
+            )
+
+            bt_checkpoint_refresh.click(fn=get_checkpoints_project, inputs=[cm_project], outputs=[cm_checkpoint])
+            cm_project.change(fn=get_checkpoints_project, inputs=[cm_project], outputs=[cm_checkpoint])
+
+        with gr.TabItem("system info"):
+            output_box = gr.Textbox(label="GPU and CPU Information", lines=20)
+
+            def update_stats():
+                return get_combined_stats()
+
+            update_button = gr.Button("Update Stats")
+            update_button.click(fn=update_stats, outputs=output_box)
+
+            def auto_update():
+                yield gr.update(value=update_stats())
+
+            gr.update(fn=auto_update, inputs=[], outputs=output_box)
+
+
+@click.command()
+@click.option("--port", "-p", default=None, type=int, help="Port to run the app on")
+@click.option("--host", "-H", default=None, help="Host to run the app on")
+@click.option(
+    "--share",
+    "-s",
+    default=False,
+    is_flag=True,
+    help="Share the app via Gradio share link",
+)
+@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access")
+def main(port, host, share, api):
+    global app
+    print("Starting app...")
+    app.queue(api_open=api).launch(server_name=host, server_port=port, share=share, show_api=api)
+
+
+if __name__ == "__main__":
+    main()

src/f5_tts/train/train.py

@@ -0,0 +1,96 @@
+# training script.
+
+from importlib.resources import files
+
+from f5_tts.model import CFM, UNetT, DiT, Trainer
+from f5_tts.model.utils import get_tokenizer
+from f5_tts.model.dataset import load_dataset
+
+
+# -------------------------- Dataset Settings --------------------------- #
+
+target_sample_rate = 24000
+n_mel_channels = 100
+hop_length = 256
+
+tokenizer = "pinyin"  # 'pinyin', 'char', or 'custom'
+tokenizer_path = None  # if tokenizer = 'custom', define the path to the tokenizer you want to use (should be vocab.txt)
+dataset_name = "Emilia_ZH_EN"
+
+# -------------------------- Training Settings -------------------------- #
+
+exp_name = "F5TTS_Base"  # F5TTS_Base | E2TTS_Base
+
+learning_rate = 7.5e-5
+
+batch_size_per_gpu = 38400  # 8 GPUs, 8 * 38400 = 307200
+batch_size_type = "frame"  # "frame" or "sample"
+max_samples = 64  # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models
+grad_accumulation_steps = 1  # note: updates = steps / grad_accumulation_steps
+max_grad_norm = 1.0
+
+epochs = 11  # use linear decay, thus epochs control the slope
+num_warmup_updates = 20000  # warmup steps
+save_per_updates = 50000  # save checkpoint per steps
+last_per_steps = 5000  # save last checkpoint per steps
+
+# model params
+if exp_name == "F5TTS_Base":
+    wandb_resume_id = None
+    model_cls = DiT
+    model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+elif exp_name == "E2TTS_Base":
+    wandb_resume_id = None
+    model_cls = UNetT
+    model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+
+
+# ----------------------------------------------------------------------- #
+
+
+def main():
+    if tokenizer == "custom":
+        tokenizer_path = tokenizer_path
+    else:
+        tokenizer_path = dataset_name
+    vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer)
+
+    mel_spec_kwargs = dict(
+        target_sample_rate=target_sample_rate,
+        n_mel_channels=n_mel_channels,
+        hop_length=hop_length,
+    )
+
+    model = CFM(
+        transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels),
+        mel_spec_kwargs=mel_spec_kwargs,
+        vocab_char_map=vocab_char_map,
+    )
+
+    trainer = Trainer(
+        model,
+        epochs,
+        learning_rate,
+        num_warmup_updates=num_warmup_updates,
+        save_per_updates=save_per_updates,
+        checkpoint_path=str(files("f5_tts").joinpath(f"../../ckpts/{exp_name}")),
+        batch_size=batch_size_per_gpu,
+        batch_size_type=batch_size_type,
+        max_samples=max_samples,
+        grad_accumulation_steps=grad_accumulation_steps,
+        max_grad_norm=max_grad_norm,
+        wandb_project="CFM-TTS",
+        wandb_run_name=exp_name,
+        wandb_resume_id=wandb_resume_id,
+        last_per_steps=last_per_steps,
+    )
+
+    train_dataset = load_dataset(dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs)
+    trainer.train(
+        train_dataset,
+        resumable_with_seed=666,  # seed for shuffling dataset
+    )
+
+
+if __name__ == "__main__":
+    main()