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Dockerfile

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+FROM python:3.11
+
+# By using XTTS you agree to CPML license https://coqui.ai/cpml
+ENV COQUI_TOS_AGREED=1
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Install dependencies
+COPY --chown=user:user requirements.txt .
+RUN pip install -r requirements.txt
+RUN python -m unidic download
+
+# Install model weights
+COPY --chown=user:user . .
+RUN python build.py
+
+CMD ["bash", "-c", "python --version && python app.py"]

README.md

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+---
+title: XTTS
+emoji: 🐸
+colorFrom: green
+colorTo: red
+pinned: false
+sdk: docker
+models:
+- coqui/XTTS-v2
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+import subprocess
+import uuid
+import time
+import torch
+import torchaudio
+
+# 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 csv
+from io import StringIO
+import datetime
+import re
+
+import gradio as gr
+
+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
+
+print("application starting")
+
+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 = "JacobLinCool/xtts-v2"
+
+model = None
+supported_languages = None
+
+
+def load_model():
+    global model
+    global supported_languages
+
+    print("loading model")
+
+    model_name = "tts_models/multilingual/multi-dataset/xtts_v2"
+    model_path = os.path.join(get_user_data_dir("tts"), model_name.replace("/", "--"))
+
+    config = XttsConfig()
+    config.load_json(os.path.join(model_path, "config.json"))
+
+    model = Xtts.init_from_config(config)
+    model.load_checkpoint(
+        config,
+        checkpoint_path=os.path.join(model_path, "model.pth"),
+        vocab_path=os.path.join(model_path, "vocab.json"),
+        eval=True,
+        use_deepspeed=False,
+    )
+
+    if torch.cuda.is_available():
+        model.cuda()
+    else:
+        model.cpu()
+
+    supported_languages = config.languages
+
+    print("Model loaded")
+
+
+# This is for debugging purposes only
+DEVICE_ASSERT_DETECTED = 0
+DEVICE_ASSERT_PROMPT = None
+DEVICE_ASSERT_LANG = None
+
+
+def predict(
+    prompt,
+    language,
+    audio_file_pth,
+    voice_cleanup,
+    no_lang_auto_detect,
+    agree,
+):
+    if model is None:
+        load_model()
+
+    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,
+                )
+
+        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, 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,
+                )
+
+            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                
+            )
+            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,
+                    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:
+        gr.Warning("Please accept the Terms & Condition!")
+        return (
+            None,
+            None,
+            None,
+            None,
+        )
+
+
+title = "Coqui🐸 XTTS"
+
+description = """
+
+<br/>
+
+<a href="https://huggingface.co/coqui/XTTS-v2">XTTS</a> is a text-to-speech model that lets you clone voices into different languages.
+
+<br/>
+
+This is the same model that powers our creator application <a href="https://coqui.ai">Coqui Studio</a> as well as the <a href="https://docs.coqui.ai">Coqui API</a>. In production we apply modifications to make low-latency streaming possible.
+
+<br/>
+
+There are 16 languages.
+
+<p>
+Arabic: ar, Brazilian 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 <br/>
+</p>
+
+<br/>
+
+Leave a star 🌟 on the Github <a href="https://github.com/coqui-ai/TTS">🐸TTS</a>, where our open-source inference and training code lives.
+
+<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>
+"""
+
+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"/>
+                """
+            )
+        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():
+            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",
+                ],
+                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",
+            )
+            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 Coqui Public Model License at 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")
+
+    tts_button.click(
+        predict,
+        [input_text_gr, language_gr, ref_gr, clean_ref_gr, auto_det_lang_gr, tos_gr],
+        outputs=[video_gr, audio_gr, out_text_gr, ref_audio_gr],
+    )
+
+print("Starting server")
+demo.queue().launch(debug=True, show_api=True)

build.py

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+import os, stat
+from zipfile import ZipFile
+
+# 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
+
+model_name = "tts_models/multilingual/multi-dataset/xtts_v2"
+ModelManager().download_model(model_name)
+print("XTTS downloaded")

gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/female.wav filter=lfs diff=lfs merge=lfs -text

packages.txt

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+unzip

requirements.txt

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+# Preinstall requirements from TTS 
+TTS @ git+https://github.com/coqui-ai/[email protected]
+pydantic==1.10.13
+python-multipart==0.0.6
+typing-extensions>=4.8.0
+cutlet
+mecab-python3==1.0.6
+unidic-lite==1.0.8
+unidic==1.1.0
+langid
+pydub
+gradio