Spaces:
Running
on
Zero
Running
on
Zero
Commit
·
04143a9
0
Parent(s):
- .gitattributes +35 -0
- .gitignore +2 -0
- README.md +53 -0
- app.py +130 -0
- requirements.txt +4 -0
.gitattributes
ADDED
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
4 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
5 |
+
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
11 |
+
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
12 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
13 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
14 |
+
*.npy filter=lfs diff=lfs merge=lfs -text
|
15 |
+
*.npz filter=lfs diff=lfs merge=lfs -text
|
16 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
17 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
18 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
19 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
20 |
+
*.pickle filter=lfs diff=lfs merge=lfs -text
|
21 |
+
*.pkl filter=lfs diff=lfs merge=lfs -text
|
22 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
30 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
31 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
32 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
33 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
34 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
.gitignore
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
lib/__pycache__
|
2 |
+
./gradio
|
README.md
ADDED
@@ -0,0 +1,53 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
title: TTS Dhivehi Demo - MMS-TTS
|
3 |
+
emoji: ⚡
|
4 |
+
colorFrom: indigo
|
5 |
+
colorTo: purple
|
6 |
+
sdk: gradio
|
7 |
+
sdk_version: 5.25.2
|
8 |
+
app_file: app.py
|
9 |
+
pinned: false
|
10 |
+
---
|
11 |
+
|
12 |
+
### Fine-tuned Text-to-Speech Model for Divehi
|
13 |
+
|
14 |
+
#### Overview
|
15 |
+
|
16 |
+
This project involves fine-tuning a Text-to-Speech (TTS) model specifically for the Divehi language using HuggingFace's powerful tools and pre-trained models, including the Massively Multilingual Speech (MMS) and VITS frameworks. Divehi, also known as Maldivian, is an Indo-Aryan language spoken in the Maldives. The aim of this fine-tuning process is to improve the TTS system's ability to generate natural and accurate Divehi speech from text inputs.
|
17 |
+
|
18 |
+
#### Model Description
|
19 |
+
|
20 |
+
**Base Models:**
|
21 |
+
- **Massively Multilingual Speech (MMS):** A pre-trained model from Facebook AI Research designed to handle TTS tasks across multiple languages. MMS provides a robust foundation with extensive language support and pre-learned phonetic nuances.
|
22 |
+
- **VITS (Variational Inference and Text-to-Speech):** A state-of-the-art TTS model that integrates variational inference and generative adversarial networks (GANs) to produce high-quality, natural-sounding speech.
|
23 |
+
|
24 |
+
**Fine-tuning Process:**
|
25 |
+
1. **Data Collection and Preparation:**
|
26 |
+
- **Text Corpus:** Compilation of a large and diverse Divehi text corpus to capture the language's phonetic and syntactic properties.
|
27 |
+
- **Audio Samples:** Collection of high-quality Divehi audio recordings, including:
|
28 |
+
- **Common Voice Dataset:** Leveraging Mozilla's Common Voice dataset, which includes a substantial number of Divehi audio samples.
|
29 |
+
- **Synthesized Data:** Utilizing over 16 hours of synthesized Divehi speech data to augment the training set.
|
30 |
+
- **Alignment:** Ensuring the text-audio pairs are accurately aligned for effective training.
|
31 |
+
|
32 |
+
2. **Training Setup:**
|
33 |
+
- **HuggingFace's Transformers Library:** Utilizing HuggingFace’s easy-to-use interface for loading pre-trained models and managing the fine-tuning process.
|
34 |
+
- **Tokenization:** Employing a tokenizer suitable for Divehi to convert text into token sequences that the model can process.
|
35 |
+
- **Model Configuration:** Adjusting model parameters to optimize performance for Divehi, including learning rates, batch sizes, and epochs.
|
36 |
+
|
37 |
+
3. **Fine-tuning:**
|
38 |
+
- **VITS Fine-tuning:** Leveraging the VITS model’s architecture to fine-tune on Divehi-specific text-audio pairs, focusing on improving the model’s ability to generate Divehi phonetics accurately.
|
39 |
+
- **MMS Fine-tuning:** Further fine-tuning the MMS model to enhance its multilingual capabilities with a specific focus on Divehi.
|
40 |
+
|
41 |
+
4. **Evaluation and Testing:**
|
42 |
+
- **Quality Assessment:** Using objective metrics like Mean Opinion Score (MOS) and subjective listening tests to evaluate the naturalness and accuracy of the generated Divehi speech.
|
43 |
+
- **Error Analysis:** Identifying and rectifying common errors such as mispronunciations, intonation issues, and unnatural pacing.
|
44 |
+
|
45 |
+
#### Benefits
|
46 |
+
|
47 |
+
- **High-Quality Speech Synthesis:** Produces natural and intelligible Divehi speech, suitable for applications in virtual assistants, audiobooks, and accessibility tools.
|
48 |
+
- **Cultural Preservation:** Supports the digital presence and preservation of the Divehi language through advanced speech technology.
|
49 |
+
- **Customizability:** Fine-tuning allows for further adjustments and improvements based on specific use cases and user feedback.
|
50 |
+
|
51 |
+
#### Conclusion
|
52 |
+
|
53 |
+
The fine-tuned Divehi Text-to-Speech model represents a significant advancement in the accessibility and usability of speech technology for the Divehi-speaking community. By combining the strengths of the MMS and VITS models with the flexibility of HuggingFace's tools, and leveraging a rich dataset including the Common Voice dataset and synthesized data, this project delivers a high-quality, linguistically accurate TTS solution tailored to the unique characteristics of the Divehi language.
|
app.py
ADDED
@@ -0,0 +1,130 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import gradio as gr
|
2 |
+
import torch
|
3 |
+
from transformers import VitsTokenizer, VitsModel, set_seed
|
4 |
+
import tempfile
|
5 |
+
import numpy as np
|
6 |
+
from scipy.io.wavfile import write
|
7 |
+
from dv_normalize.dv_sentence import spoken_dv
|
8 |
+
|
9 |
+
# HuggingFace models with default seeds
|
10 |
+
models = {
|
11 |
+
"MMS TTS Base": {"model": "alakxender/mms-tts-div", "seed": 555},
|
12 |
+
"Female F01 (CV)": {"model": "alakxender/mms-tts-div-finetuned-md-f01", "seed": 555},
|
13 |
+
"Female F02 (CV, pitch/tempo changed)": {"model": "alakxender/mms-tts-div-finetuned-md-f02", "seed": 555},
|
14 |
+
"Female F03 (CV, pitch/tempo changed)": {"model": "alakxender/mms-tts-div-finetuned-md-f03", "seed": 555},
|
15 |
+
"Female F04 (CV, rvc-test)": {"model": "alakxender/mms-tts-speak-f01", "seed": 555},
|
16 |
+
"Female F01 (z-test)": {"model": "alakxender/mms-tts-div-ft-spk01-f01", "seed": 555},
|
17 |
+
#"Female Unknown 👩🏽 (🤷♀️)": {"model": "alakxender/mms-tts-div-finetuned-sm-fu01", "seed": 555},
|
18 |
+
"Male M01 (CV) 👨🏽": {"model": "alakxender/mms-tts-div-finetuned-md-m01", "seed": 555},
|
19 |
+
#"Male M02 (javaabu/shaafiu)": {"model": "alakxender/mms-tts-div-finetuned-sm-mu01", "seed": 555},
|
20 |
+
"Male M02 (z-test)": {"model": "alakxender/mms-tts-div-ft-spk01-m01", "seed": 620},
|
21 |
+
"Male M02 (z-test)1": {"model": "alakxender/mms-tts-div-finetuned-m-spk01-t1", "seed": 555}
|
22 |
+
}
|
23 |
+
|
24 |
+
def tts(text: str, model_name: str, seed_value: int = None):
|
25 |
+
if (len(text) > 2000):
|
26 |
+
raise gr.Error(f"huh! using free cpu here!, try a small chunk of data. Yours is {len(text)}. try to fit to 2000 chars.")
|
27 |
+
if (model_name is None):
|
28 |
+
raise gr.Error("huh! not sure what to do without a model. select a model.")
|
29 |
+
|
30 |
+
# Use default seed if none provided
|
31 |
+
if seed_value is None:
|
32 |
+
seed_value = models[model_name]["seed"]
|
33 |
+
|
34 |
+
print(f"Loading...{models[model_name]['model']}")
|
35 |
+
# Load the MMS-TTS model
|
36 |
+
tokenizer = VitsTokenizer.from_pretrained(models[model_name]["model"])
|
37 |
+
model = VitsModel.from_pretrained(models[model_name]["model"])
|
38 |
+
print("Model loaded.")
|
39 |
+
|
40 |
+
# normalize the dv text from written to spoken
|
41 |
+
print(f"Normalizing: {text}")
|
42 |
+
text = spoken_dv(text)
|
43 |
+
print(f"Normalized: {text}")
|
44 |
+
|
45 |
+
# Preprocess the input text
|
46 |
+
inputs = tokenizer(text=text, return_tensors="pt")
|
47 |
+
print("Preprocess done.")
|
48 |
+
|
49 |
+
# Make the speech synthesis deterministic with user-defined seed
|
50 |
+
print(f"Setting seed to: {seed_value}")
|
51 |
+
set_seed(seed_value)
|
52 |
+
|
53 |
+
# Generate the audio waveform
|
54 |
+
print("Generating audio...")
|
55 |
+
with torch.no_grad():
|
56 |
+
outputs = model(**inputs)
|
57 |
+
waveform = outputs.waveform[0]
|
58 |
+
sample_rate = model.config.sampling_rate
|
59 |
+
|
60 |
+
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
|
61 |
+
# Save the waveform to the temporary file
|
62 |
+
write(f.name, sample_rate, waveform.numpy().T)
|
63 |
+
# Get the file name
|
64 |
+
waveform_file = f.name
|
65 |
+
print("done.")
|
66 |
+
return waveform_file
|
67 |
+
|
68 |
+
def get_default_seed(model_name):
|
69 |
+
return models[model_name]["seed"]
|
70 |
+
|
71 |
+
css = """
|
72 |
+
.textbox1 textarea {
|
73 |
+
font-size: 18px !important;
|
74 |
+
font-family: 'MV_Faseyha', 'Faruma', 'A_Faruma' !important;
|
75 |
+
line-height: 1.8 !important;
|
76 |
+
}
|
77 |
+
"""
|
78 |
+
|
79 |
+
with gr.Blocks(css=css) as demo:
|
80 |
+
gr.Markdown("# <center> DV Text-To-Speech </center>")
|
81 |
+
gr.Markdown("This interface converts Divehi text into natural-sounding speech using a fine-tuned Text-to-Speech model. Leveraging the capabilities of Massively Multilingual Speech (MMS) and VITS models. Text normalization is also incorporated to handle various input formats effectively.")
|
82 |
+
|
83 |
+
with gr.Row():
|
84 |
+
with gr.Column(scale=3):
|
85 |
+
text = gr.TextArea(
|
86 |
+
label="Input text",
|
87 |
+
placeholder="ދިވެހި ބަހުން ކޮންމެވެސް އެއްޗެކޭ މިތާ ލިޔެބަލަ",
|
88 |
+
rtl=True,
|
89 |
+
elem_classes="textbox1"
|
90 |
+
)
|
91 |
+
with gr.Column(scale=1):
|
92 |
+
model_name = gr.Dropdown(
|
93 |
+
choices=list(models.keys()),
|
94 |
+
label="Select TTS Model",
|
95 |
+
value=list(models.keys())[5] # Default to first model
|
96 |
+
)
|
97 |
+
seed_slider = gr.Slider(
|
98 |
+
minimum=0,
|
99 |
+
maximum=1000,
|
100 |
+
value=555, # Default value
|
101 |
+
step=1,
|
102 |
+
label="Seed Value (affects voice variation)"
|
103 |
+
)
|
104 |
+
|
105 |
+
# Update seed slider when model changes
|
106 |
+
model_name.change(
|
107 |
+
fn=get_default_seed,
|
108 |
+
inputs=[model_name],
|
109 |
+
outputs=[seed_slider]
|
110 |
+
)
|
111 |
+
|
112 |
+
btn = gr.Button("Text-To-Speech")
|
113 |
+
output_audio = gr.Audio(label="Speech Output")
|
114 |
+
|
115 |
+
# Add examples section
|
116 |
+
with gr.Accordion("Examples", open=True):
|
117 |
+
example_text = "އައްޑޫގެ ގުޅިފައިވާ ރަށްތަކުގައި އެންމެ މަތިން ކަރަންޓު ބޭނުންވާ ގަޑިތަކުގައި 12 މެގަވޮޓްގެ ކަރަންޓު ބޭނުންވެ އެވެ. ކަރަންޓު ފޯރުކޮށްދިނުމަށް ހިތަދޫގައި ބ��ހައްޓާފައި ވަނީ 20 ޖެނަރޭޓަރު ސެޓެވެ. އޭގެ ކެޕޭސިޓީއަކީ 26.8 މެގަވޮޓެވެ. އެކަމަކު އޭގެ ތެރެއިން ފަސް ޖެނަރޭޓަރު ހަލާކުވުމާ ގުޅިގެން އޭރު އުފެއްދުނީ 15 މެގަވޮޓެވެ."
|
118 |
+
gr.Examples(
|
119 |
+
[[example_text, list(models.keys())[5], models[list(models.keys())[5]]["seed"]]],
|
120 |
+
[text, model_name, seed_slider],
|
121 |
+
fn=tts,
|
122 |
+
outputs=output_audio
|
123 |
+
)
|
124 |
+
|
125 |
+
text.submit(fn=tts, inputs=[text, model_name, seed_slider], outputs=output_audio)
|
126 |
+
btn.click(fn=tts, inputs=[text, model_name, seed_slider], outputs=output_audio)
|
127 |
+
|
128 |
+
# Launch the Gradio app
|
129 |
+
if __name__ == "__main__":
|
130 |
+
demo.launch()
|
requirements.txt
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
dv-normalizer
|
2 |
+
torch
|
3 |
+
transformers
|
4 |
+
scipy
|