Update app.py

app.py

@@ -1,77 +1,696 @@
+import sys
+import io, os, stat
+import subprocess
+import random
+from zipfile import ZipFile
+import uuid
+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
+from io import StringIO
+import datetime
+import re
+
 import gradio as gr
+from scipy.io.wavfile import write
+from pydub import AudioSegment
+
 from TTS.api import TTS
 from TTS.tts.configs.xtts_config import XttsConfig
 from TTS.tts.models.xtts import Xtts
 from TTS.utils.generic_utils import get_user_data_dir
-import os
+
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
 from huggingface_hub import hf_hub_download
+import os
+from TTS.utils.manage import get_user_data_dir
 
-# Configuración de rutas y descarga del modelo
+# Autenticación con la API de Hugging Face
 repo_id = "Blakus/Pedro_Lab_XTTS"
 local_dir = os.path.join(get_user_data_dir("tts"), "tts_models--multilingual--multi-dataset--xtts_v2")
+
+# Crear el directorio si no existe
 os.makedirs(local_dir, exist_ok=True)
 
+# Lista de archivos necesarios
 files_to_download = ["config.json", "model.pth", "vocab.json"]
+
+# Descargar cada archivo del repositorio
 for file_name in files_to_download:
+    print(f"Downloading {file_name} from {repo_id}")
+    local_file_path = os.path.join(local_dir, file_name)
     hf_hub_download(repo_id=repo_id, filename=file_name, local_dir=local_dir)
 
+# Cargar configuración y modelo
 config_path = os.path.join(local_dir, "config.json")
 checkpoint_path = os.path.join(local_dir, "model.pth")
 vocab_path = os.path.join(local_dir, "vocab.json")
 
-# Cargar el modelo XTTS
 config = XttsConfig()
 config.load_json(config_path)
+
 model = Xtts.init_from_config(config)
-model.load_checkpoint(config, checkpoint_path=checkpoint_path, vocab_path=vocab_path, eval=True, use_deepspeed=False)
+model.load_checkpoint(
+    config,
+    checkpoint_path=checkpoint_path,
+    vocab_path=vocab_path,
+    eval=True,
+    use_deepspeed=False,  # Desactivamos DeepSpeed para ejecución en CPU
+)
+
+# No transfieras el modelo a la GPU
+print("Modelo cargado en CPU")
+
+# This is for debugging purposes only
+DEVICE_ASSERT_DETECTED = 0
+DEVICE_ASSERT_PROMPT = None
+DEVICE_ASSERT_LANG = None
+
+supported_languages = config.languages
+
+def predict(
+    prompt,
+    language,
+    audio_file_pth,
+    mic_file_path,
+    use_mic,
+    voice_cleanup,
+    no_lang_auto_detect,
+    agree,
+):
+    if agree == True:
+        if language not in supported_languages:
+            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,
+            )
+
+        language_predicted = langid.classify(prompt)[
+            0
+        ].strip()  # strip need as there is space at end!
+
+        # tts expects chinese as zh-cn
+        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,
+                )
+
+        if use_mic == True:
+            if mic_file_path is not None:
+                speaker_wav = mic_file_path
+            else:
+                gr.Warning(
+                    "Please record your voice with Microphone, or uncheck Use Microphone to use reference audios"
+                )
+                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:
+            lowpass_highpass = "lowpass=8000,highpass=75,"
+        else:
+            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(
+                    " "
+                )
+
+                command_result = subprocess.run(
+                    [item for item in shell_command],
+                    capture_output=False,
+                    text=True,
+                    check=True,
+                )
+                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:
+            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,
+            )
+        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
+            space = api.get_space_runtime(repo_id=repo_id)
+            if space.stage!="BUILDING":
+                api.restart_space(repo_id=repo_id)
+            else:
+                print("TRIED TO RESTART but space is building")
+
+        try:
+            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)
+            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,
+                )
 
-def sintetizar_voz(texto, idioma, audio_referencia, usar_microfono, audio_microfono):
-    if usar_microfono:
-        audio_entrada = audio_microfono
+            latent_calculation_time = time.time() - t_latent
+            # metrics_text=f"Embedding calculation time: {latent_calculation_time:.2f} seconds\n"
+
+            # 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(
+                prompt,
+                language,
+                gpt_cond_latent,
+                speaker_embedding,
+                repetition_penalty=5.0,
+                temperature=0.75,
+            )
+            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"
+            real_time_factor= (time.time() - t0) / out['wav'].shape[-1] * 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", 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,
+                )
+                gr.Warning("Unhandled Exception encounter, please retry in a minute")
+                print("Cuda device-assert Runtime encountered need restart")
+                if not DEVICE_ASSERT_DETECTED:
+                    DEVICE_ASSERT_DETECTED = 1
+                    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,
+                    prompt,
+                    language,
+                    audio_file_pth,
+                    mic_file_path,
+                    use_mic,
+                    voice_cleanup,
+                    no_lang_auto_detect,
+                    agree,
+                ]
+                error_data = [str(e) if type(e) != str else e for e in error_data]
+                print(error_data)
+                print(speaker_wav)
+                write_io = StringIO()
+                csv.writer(write_io).writerows([error_data])
+                csv_upload = write_io.getvalue().encode()
+
+                filename = error_time + "_" + str(uuid.uuid4()) + ".csv"
+                print("Writing error csv")
+                error_api = HfApi()
+                error_api.upload_file(
+                    path_or_fileobj=csv_upload,
+                    path_in_repo=filename,
+                    repo_id="coqui/xtts-flagged-dataset",
+                    repo_type="dataset",
+                )
+
+                # speaker_wav
+                print("Writing error reference audio")
+                speaker_filename = (
+                    error_time + "_reference_" + str(uuid.uuid4()) + ".wav"
+                )
+                error_api = HfApi()
+                error_api.upload_file(
+                    path_or_fileobj=speaker_wav,
+                    path_in_repo=speaker_filename,
+                    repo_id="coqui/xtts-flagged-dataset",
+                    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)
+                else:
+                    print("TRIED TO RESTART but space is building")
+                    
+            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?"
+                    )
+                else:
+                    print("RuntimeError: non device-side assert error:", str(e))
+                    gr.Warning("Something unexpected happened please retry again.")
+                return (
+                    None,
+                    None,
+                    None,
+                    None,
+                )
+        return (
+            gr.make_waveform(
+                audio="output.wav",
+            ),
+            "output.wav",
+            metrics_text,
+            speaker_wav,
+        )
     else:
-        audio_entrada = audio_referencia
-    
-    # Lógica de síntesis de voz usando el modelo XTTS
-    gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=audio_entrada, gpt_cond_len=30, gpt_cond_chunk_len=4, max_ref_length=60)
-    out = model.inference(
-        texto,
-        language=idioma,
-        gpt_cond_latent=gpt_cond_latent,
-        speaker_embedding=speaker_embedding,
-        repetition_penalty=5.0,
-        temperature=0.75,
-    )
-    
-    # Guardar el audio generado
-    output_path = "output.wav"
-    model.save_wav(wav=out["wav"], path=output_path)
-    
-    return output_path, f"Tiempo de generación: {out['inference_time']:.2f} segundos"
-
-with gr.Blocks(theme=gr.themes.Base()) as demo:
-    gr.Markdown("# Sintetizador de Voz XTTS")
-    
+        gr.Warning("Please accept the Terms & Condition!")
+        return (
+            None,
+            None,
+            None,
+            None,
+        )
+
+
+title = "Coqui🐸 XTTS"
+
+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.
+<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
+<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():
-            texto_entrada = gr.Textbox(label="Texto a sintetizar", placeholder="Escribe aquí el texto que quieres convertir a voz...")
-            idioma = gr.Dropdown(label="Idioma", choices=config.languages, value="es")
-            audio_referencia = gr.Audio(label="Audio de referencia", type="filepath")
-            usar_microfono = gr.Checkbox(label="Usar micrófono")
-            audio_microfono = gr.Audio(label="Grabar con micrófono", source="microphone", type="filepath", visible=False)
-            
-            usar_microfono.change(fn=lambda x: gr.update(visible=x), inputs=[usar_microfono], outputs=[audio_microfono])
-            
-            boton_sintetizar = gr.Button("Sintetizar")
-        
+            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)
+
+    with gr.Row():
         with gr.Column():
-            audio_salida = gr.Audio(label="Audio sintetizado")
-            waveform = gr.Image(label="Forma de onda")
-            metricas = gr.Textbox(label="Métricas")
-    
-    boton_sintetizar.click(
-        sintetizar_voz,
-        inputs=[texto_entrada, idioma, audio_referencia, usar_microfono, audio_microfono],
-        outputs=[audio_salida, metricas]
-    )
-
-demo.launch()
+            input_text_gr = gr.Textbox(
+                label="Text Prompt",
+                info="One or two sentences at a time is better. Up to 200 text characters.",
+                value="Hi there, I'm your new voice clone. Try your best to upload quality audio.",
+            )
+            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,
+                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",
+                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)
+
+
+        with gr.Column():
+            video_gr = gr.Video(label="Waveform Visual")
+            audio_gr = gr.Audio(label="Synthesised Audio", autoplay=True)
+            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])
+
+demo.queue()  
+demo.launch(debug=True, show_api=True)