Create app.py

app.py

@@ -8,15 +8,12 @@ import time
 import torch
 import torchaudio
 
-
 #download for mecab
 os.system('python -m unidic download')
 
 # By using XTTS you agree to CPML license https://coqui.ai/cpml
 os.environ["COQUI_TOS_AGREED"] = "1"
 
-# langid is used to detect language for longer text
-# Most users expect text to be their own language, there is checkbox to disable it
 import langid
 import base64
 import csv
@@ -37,18 +34,15 @@ HF_TOKEN = os.environ.get("HF_TOKEN")
 
 from huggingface_hub import HfApi
 
-# will use api to restart space on a unrecoverable error
 api = HfApi(token=HF_TOKEN)
 repo_id = "coqui/xtts"
 
-# Use never ffmpeg binary for Ubuntu20 to use denoising for microphone input
 print("Export newer ffmpeg binary for denoise filter")
 ZipFile("ffmpeg.zip").extractall()
 print("Make ffmpeg binary executable")
 st = os.stat("ffmpeg")
 os.chmod("ffmpeg", st.st_mode | stat.S_IEXEC)
 
-# This will trigger downloading model
 print("Downloading if not downloaded Coqui XTTS V2")
 from TTS.utils.manage import ModelManager
 
@@ -70,7 +64,6 @@ model.load_checkpoint(
 )
 model.cuda()
 
-# This is for debugging purposes only
 DEVICE_ASSERT_DETECTED = 0
 DEVICE_ASSERT_PROMPT = None
 DEVICE_ASSERT_LANG = None
@@ -92,43 +85,20 @@ def predict(
             gr.Warning(
                 f"Language you put {language} in is not in is not in our Supported Languages, please choose from dropdown"
             )
+            return (None, None, None, None)
 
-            return (
-                None,
-                None,
-                None,
-                None,
-            )
-
-        language_predicted = langid.classify(prompt)[
-            0
-        ].strip()  # strip need as there is space at end!
-
-        # tts expects chinese as zh-cn
+        language_predicted = langid.classify(prompt)[0].strip()
         if language_predicted == "zh":
-            # we use zh-cn
             language_predicted = "zh-cn"
 
         print(f"Detected language:{language_predicted}, Chosen language:{language}")
 
-        # After text character length 15 trigger language detection
         if len(prompt) > 15:
-            # allow any language for short text as some may be common
-            # If user unchecks language autodetection it will not trigger
-            # You may remove this completely for own use
             if language_predicted != language and not no_lang_auto_detect:
-                # Please duplicate and remove this check if you really want this
-                # Or auto-detector fails to identify language (which it can on pretty short text or mixed text)
                 gr.Warning(
-                    f"It looks like your text isn’t the language you chose , if you’re sure the text is the same language you chose, please check disable language auto-detection checkbox"
-                )
-
-                return (
-                    None,
-                    None,
-                    None,
-                    None,
+                    f"It looks like your text isn't the language you chose, if you're sure the text is the same language you chose, please check disable language auto-detection checkbox"
                 )
+                return (None, None, None, None)
 
         if use_mic == True:
             if mic_file_path is not None:
@@ -137,20 +107,10 @@ def predict(
                 gr.Warning(
                     "Please record your voice with Microphone, or uncheck Use Microphone to use reference audios"
                 )
-                return (
-                    None,
-                    None,
-                    None,
-                    None,
-                )
-
+                return (None, None, None, None)
         else:
             speaker_wav = audio_file_pth
 
-        # Filtering for microphone input, as it has BG noise, maybe silence in beginning and end
-        # This is fast filtering not perfect
-
-        # Apply all on demand
         lowpassfilter = denoise = trim = loudness = True
 
         if lowpassfilter:
@@ -159,22 +119,14 @@ def predict(
             lowpass_highpass = ""
 
         if trim:
-            # better to remove silence in beginning and end for microphone
             trim_silence = "areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,"
         else:
             trim_silence = ""
 
         if voice_cleanup:
             try:
-                out_filename = (
-                    speaker_wav + str(uuid.uuid4()) + ".wav"
-                )  # ffmpeg to know output format
-
-                # we will use newer ffmpeg as that has afftn denoise filter
-                shell_command = f"./ffmpeg -y -i {speaker_wav} -af {lowpass_highpass}{trim_silence} {out_filename}".split(
-                    " "
-                )
-
+                out_filename = speaker_wav + str(uuid.uuid4()) + ".wav"
+                shell_command = f"./ffmpeg -y -i {speaker_wav} -af {lowpass_highpass}{trim_silence} {out_filename}".split(" ")
                 command_result = subprocess.run(
                     [item for item in shell_command],
                     capture_output=False,
@@ -184,39 +136,26 @@ def predict(
                 speaker_wav = out_filename
                 print("Filtered microphone input")
             except subprocess.CalledProcessError:
-                # There was an error - command exited with non-zero code
                 print("Error: failed filtering, use original microphone input")
         else:
             speaker_wav = speaker_wav
 
         if len(prompt) < 2:
             gr.Warning("Please give a longer prompt text")
-            return (
-                None,
-                None,
-                None,
-                None,
-            )
-        if len(prompt) > 200:
+            return (None, None, None, None)
+            
+        # Changed from 200 to 5000 characters
+        if len(prompt) > 5000:
             gr.Warning(
-                "Text length limited to 200 characters for this demo, please try shorter text. You can clone this space and edit code for your own usage"
-            )
-            return (
-                None,
-                None,
-                None,
-                None,
+                "Text length limited to 5000 characters for this demo"
             )
+            return (None, None, None, None)
+
         global DEVICE_ASSERT_DETECTED
         if DEVICE_ASSERT_DETECTED:
             global DEVICE_ASSERT_PROMPT
             global DEVICE_ASSERT_LANG
-            # It will likely never come here as we restart space on first unrecoverable error now
-            print(
-                f"Unrecoverable exception caused by language:{DEVICE_ASSERT_LANG} prompt:{DEVICE_ASSERT_PROMPT}"
-            )
-
-            # HF Space specific.. This error is unrecoverable need to restart space
+            print(f"Unrecoverable exception caused by language:{DEVICE_ASSERT_LANG} prompt:{DEVICE_ASSERT_PROMPT}")
             space = api.get_space_runtime(repo_id=repo_id)
             if space.stage!="BUILDING":
                 api.restart_space(repo_id=repo_id)
@@ -227,33 +166,21 @@ def predict(
             metrics_text = ""
             t_latent = time.time()
 
-            # note diffusion_conditioning not used on hifigan (default mode), it will be empty but need to pass it to model.inference
             try:
-                (
-                    gpt_cond_latent,
-                    speaker_embedding,
-                ) = model.get_conditioning_latents(audio_path=speaker_wav, gpt_cond_len=30, gpt_cond_chunk_len=4, max_ref_length=60)
+                (gpt_cond_latent, speaker_embedding) = model.get_conditioning_latents(
+                    audio_path=speaker_wav, 
+                    gpt_cond_len=30, 
+                    gpt_cond_chunk_len=4, 
+                    max_ref_length=60
+                )
             except Exception as e:
                 print("Speaker encoding error", str(e))
-                gr.Warning(
-                    "It appears something wrong with reference, did you unmute your microphone?"
-                )
-                return (
-                    None,
-                    None,
-                    None,
-                    None,
-                )
+                gr.Warning("It appears something wrong with reference, did you unmute your microphone?")
+                return (None, None, None, None)
 
             latent_calculation_time = time.time() - t_latent
-            # metrics_text=f"Embedding calculation time: {latent_calculation_time:.2f} seconds\n"
+            prompt = re.sub("([^\x00-\x7F]|\w)(\.|\。|\?)",r"\1 \2\2",prompt)
 
-            # temporary comma fix
-            prompt= re.sub("([^\x00-\x7F]|\w)(\.|\。|\?)",r"\1 \2\2",prompt)
-
-            wav_chunks = []
-            ## Direct mode
-            
             print("I: Generating new audio...")
             t0 = time.time()
             out = model.inference(
@@ -272,51 +199,9 @@ def predict(
             metrics_text+=f"Real-time factor (RTF): {real_time_factor:.2f}\n"
             torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
 
-
-            """
-            print("I: Generating new audio in streaming mode...")
-            t0 = time.time()
-            chunks = model.inference_stream(
-                prompt,
-                language,
-                gpt_cond_latent,
-                speaker_embedding,
-                repetition_penalty=7.0,
-                temperature=0.85,
-            )
-
-            first_chunk = True
-            for i, chunk in enumerate(chunks):
-                if first_chunk:
-                    first_chunk_time = time.time() - t0
-                    metrics_text += f"Latency to first audio chunk: {round(first_chunk_time*1000)} milliseconds\n"
-                    first_chunk = False
-                wav_chunks.append(chunk)
-                print(f"Received chunk {i} of audio length {chunk.shape[-1]}")
-            inference_time = time.time() - t0
-            print(
-                f"I: Time to generate audio: {round(inference_time*1000)} milliseconds"
-            )
-            #metrics_text += (
-            #    f"Time to generate audio: {round(inference_time*1000)} milliseconds\n"
-            #)
-
-            wav = torch.cat(wav_chunks, dim=0)
-            print(wav.shape)
-            real_time_factor = (time.time() - t0) / wav.shape[0] * 24000
-            print(f"Real-time factor (RTF): {real_time_factor}")
-            metrics_text += f"Real-time factor (RTF): {real_time_factor:.2f}\n"
-
-            torchaudio.save("output.wav", wav.squeeze().unsqueeze(0).cpu(), 24000)
-            """
-
         except RuntimeError as e:
             if "device-side assert" in str(e):
-                # cannot do anything on cuda device side error, need tor estart
-                print(
-                    f"Exit due to: Unrecoverable exception caused by language:{language} prompt:{prompt}",
-                    flush=True,
-                )
+                print(f"Exit due to: Unrecoverable exception caused by language:{language} prompt:{prompt}", flush=True)
                 gr.Warning("Unhandled Exception encounter, please retry in a minute")
                 print("Cuda device-assert Runtime encountered need restart")
                 if not DEVICE_ASSERT_DETECTED:
@@ -324,8 +209,6 @@ def predict(
                     DEVICE_ASSERT_PROMPT = prompt
                     DEVICE_ASSERT_LANG = language
 
-                # just before restarting save what caused the issue so we can handle it in future
-                # Uploading Error data only happens for unrecovarable error
                 error_time = datetime.datetime.now().strftime("%d-%m-%Y-%H:%M:%S")
                 error_data = [
                     error_time,
@@ -355,11 +238,7 @@ def predict(
                     repo_type="dataset",
                 )
 
-                # speaker_wav
-                print("Writing error reference audio")
-                speaker_filename = (
-                    error_time + "_reference_" + str(uuid.uuid4()) + ".wav"
-                )
+                speaker_filename = error_time + "_reference_" + str(uuid.uuid4()) + ".wav"
                 error_api = HfApi()
                 error_api.upload_file(
                     path_or_fileobj=speaker_wav,
@@ -368,7 +247,6 @@ def predict(
                     repo_type="dataset",
                 )
 
-                # HF Space specific.. This error is unrecoverable need to restart space
                 space = api.get_space_runtime(repo_id=repo_id)
                 if space.stage!="BUILDING":
                     api.restart_space(repo_id=repo_id)
@@ -378,310 +256,92 @@ def predict(
             else:
                 if "Failed to decode" in str(e):
                     print("Speaker encoding error", str(e))
-                    gr.Warning(
-                        "It appears something wrong with reference, did you unmute your microphone?"
-                    )
+                    gr.Warning("It appears something wrong with reference, did you unmute your microphone?")
                 else:
                     print("RuntimeError: non device-side assert error:", str(e))
                     gr.Warning("Something unexpected happened please retry again.")
-                return (
-                    None,
-                    None,
-                    None,
-                    None,
-                )
+                return (None, None, None, None)
         return (
-            gr.make_waveform(
-                audio="output.wav",
-            ),
+            gr.make_waveform(audio="output.wav"),
             "output.wav",
             metrics_text,
             speaker_wav,
         )
     else:
         gr.Warning("Please accept the Terms & Condition!")
-        return (
-            None,
-            None,
-            None,
-            None,
-        )
+        return (None, None, None, None)
 
-
-title = "Coqui🐸 XTTS"
+title = "Coqui🐸 XTTS (5000 Char Limit)"
 
 description = """
-
 <br/>
-
-This demo is currently running **XTTS v2.0.3** <a href="https://huggingface.co/coqui/XTTS-v2">XTTS</a> is a multilingual text-to-speech and voice-cloning model. This demo features zero-shot voice cloning, however, you can fine-tune XTTS for better results. Leave a star 🌟 on Github <a href="https://github.com/coqui-ai/TTS">🐸TTS</a>, where our open-source inference and training code lives.
-
+This demo is running **XTTS v2.0.3** with 5000 character limit. <a href="https://huggingface.co/coqui/XTTS-v2">XTTS</a> is a multilingual text-to-speech model with voice cloning. 
 <br/>
-
-Supported languages: Arabic: ar, Brazilian Portuguese: pt , Mandarin Chinese: zh-cn, Czech: cs, Dutch: nl, English: en, French: fr, German: de, Italian: it, Polish: pl, Russian: ru, Spanish: es, Turkish: tr, Japanese: ja, Korean: ko, Hungarian: hu, Hindi: hi
-
+Supported languages: Arabic (ar), Portuguese (pt), Chinese (zh-cn), Czech (cs), Dutch (nl), English (en), French (fr), German (de), Italian (it), Polish (pl), Russian (ru), Spanish (es), Turkish (tr), Japanese (ja), Korean (ko), Hungarian (hu), Hindi (hi)
 <br/>
 """
 
-links = """
-<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=0d00920c-8cc9-4bf3-90f2-a615797e5f59" />
-
-|                                 |                                         |
-| ------------------------------- | --------------------------------------- |
-| 🐸💬 **CoquiTTS**                | <a style="display:inline-block" href='https://github.com/coqui-ai/TTS'><img src='https://img.shields.io/github/stars/coqui-ai/TTS?style=social' /></a>|
-| 💼 **Documentation**            | [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
-| 👩‍💻 **Questions**                | [GitHub Discussions](https://github.com/coqui-ai/TTS/discussions) |
-| 🗯 **Community**         | [![Dicord](https://img.shields.io/discord/1037326658807533628?color=%239B59B6&label=chat%20on%20discord)](https://discord.gg/5eXr5seRrv)  |
-
-
-"""
-
-article = """
-<div style='margin:20px auto;'>
-<p>By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml</p>
-<p>We collect data only for error cases for improvement.</p>
-</div>
-"""
-examples = [
-    [
-        "Once when I was six years old I saw a magnificent picture",
-        "en",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Lorsque j'avais six ans j'ai vu, une fois, une magnifique image",
-        "fr",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Als ich sechs war, sah ich einmal ein wunderbares Bild",
-        "de",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Cuando tenía seis años, vi una vez una imagen magnífica",
-        "es",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Quando eu tinha seis anos eu vi, uma vez, uma imagem magnífica",
-        "pt",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Kiedy miałem sześć lat, zobaczyłem pewnego razu wspaniały obrazek",
-        "pl",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Un tempo lontano, quando avevo sei anni, vidi un magnifico disegno",
-        "it",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Bir zamanlar, altı yaşındayken, muhteşem bir resim gördüm",
-        "tr",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Когда мне было шесть лет, я увидел однажды удивительную картинку",
-        "ru",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Toen ik een jaar of zes was, zag ik op een keer een prachtige plaat",
-        "nl",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Když mi bylo šest let, viděl jsem jednou nádherný obrázek",
-        "cs",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "当我还只有六岁的时候， 看到了一副精彩的插画",
-        "zh-cn",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "かつて 六歳のとき、素晴らしい絵を見ました",
-        "ja",
-        "examples/female.wav",
-        None,
-        False,
-        True,
-        False,
-        True,
-    ],
-    [
-        "한번은 내가 여섯 살이었을 때 멋진 그림을 보았습니다.",
-        "ko",
-        "examples/female.wav",
-        None,
-        False,
-        True,
-        False,
-        True,
-    ],
-        [
-        "Egyszer hat éves koromban láttam egy csodálatos képet",
-        "hu",
-        "examples/male.wav",
-        None,
-        False,
-        True,
-        False,
-        True,
-    ],
-]
-
-
-
 with gr.Blocks(analytics_enabled=False) as demo:
     with gr.Row():
         with gr.Column():
-            gr.Markdown(
-                """
-                ## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>
-                """
-            )
+            gr.Markdown("""
+            ## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>
+            """)
         with gr.Column():
-            # placeholder to align the image
             pass
 
     with gr.Row():
         with gr.Column():
             gr.Markdown(description)
         with gr.Column():
-            gr.Markdown(links)
+            gr.Markdown("""
+            |                                 |                                         |
+            | ------------------------------- | --------------------------------------- |
+            | 🐸💬 **CoquiTTS**                | <a style="display:inline-block" href='https://github.com/coqui-ai/TTS'><img src='https://img.shields.io/github/stars/coqui-ai/TTS?style=social' /></a>|
+            | 💼 **Documentation**            | [ReadTheDocs](https://tts.readthedocs.io/en/latest/) |
+            """)
 
     with gr.Row():
         with gr.Column():
             input_text_gr = gr.Textbox(
                 label="Text Prompt",
-                info="One or two sentences at a time is better. Up to 200 text characters.",
+                info="Up to 5000 text characters.",
                 value="Hi there, I'm your new voice clone. Try your best to upload quality audio.",
+                lines=5,
+                max_lines=10
             )
             language_gr = gr.Dropdown(
                 label="Language",
-                info="Select an output language for the synthesised speech",
-                choices=[
-                    "en",
-                    "es",
-                    "fr",
-                    "de",
-                    "it",
-                    "pt",
-                    "pl",
-                    "tr",
-                    "ru",
-                    "nl",
-                    "cs",
-                    "ar",
-                    "zh-cn",
-                    "ja",
-                    "ko",
-                    "hu",
-                    "hi"
-                ],
-                max_choices=1,
+                choices=["en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl", "cs", "ar", "zh-cn", "ja", "ko", "hu", "hi"],
                 value="en",
             )
             ref_gr = gr.Audio(
                 label="Reference Audio",
-                info="Click on the ✎ button to upload your own target speaker audio",
                 type="filepath",
                 value="examples/female.wav",
             )
             mic_gr = gr.Audio(
                 source="microphone",
                 type="filepath",
-                info="Use your microphone to record audio",
                 label="Use Microphone for Reference",
             )
             use_mic_gr = gr.Checkbox(
                 label="Use Microphone",
                 value=False,
-                info="Notice: Microphone input may not work properly under traffic",
             )
             clean_ref_gr = gr.Checkbox(
                 label="Cleanup Reference Voice",
                 value=False,
-                info="This check can improve output if your microphone or reference voice is noisy",
             )
             auto_det_lang_gr = gr.Checkbox(
                 label="Do not use language auto-detect",
                 value=False,
-                info="Check to disable language auto-detection",
             )
             tos_gr = gr.Checkbox(
-                label="Agree",
+                label="Agree to CPML terms",
                 value=False,
-                info="I agree to the terms of the CPML: https://coqui.ai/cpml",
             )
-
-            tts_button = gr.Button("Send", elem_id="send-btn", visible=True)
-
+            tts_button = gr.Button("Generate Speech", elem_id="send-btn", visible=True)
 
         with gr.Column():
             video_gr = gr.Video(label="Waveform Visual")
@@ -689,15 +349,11 @@ with gr.Blocks(analytics_enabled=False) as demo:
             out_text_gr = gr.Text(label="Metrics")
             ref_audio_gr = gr.Audio(label="Reference Audio Used")
 
-    with gr.Row():
-        gr.Examples(examples,
-                    label="Examples",
-                    inputs=[input_text_gr, language_gr, ref_gr, mic_gr, use_mic_gr, clean_ref_gr, auto_det_lang_gr, tos_gr],
-                    outputs=[video_gr, audio_gr, out_text_gr, ref_audio_gr],
-                    fn=predict,
-                    cache_examples=False,)
-
-    tts_button.click(predict, [input_text_gr, language_gr, ref_gr, mic_gr, use_mic_gr, clean_ref_gr, auto_det_lang_gr, tos_gr], outputs=[video_gr, audio_gr, out_text_gr, ref_audio_gr])
+    tts_button.click(
+        predict, 
+        [input_text_gr, language_gr, ref_gr, mic_gr, use_mic_gr, clean_ref_gr, auto_det_lang_gr, tos_gr], 
+        outputs=[video_gr, audio_gr, out_text_gr, ref_audio_gr]
+    )
 
 demo.queue()  
 demo.launch(debug=True, show_api=True)