app.py

import tempfile
from typing import Optional
from TTS.config import load_config
import gradio as gr
import numpy as np
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer


MODELS = {}
SPEAKERS = {}
MAX_TXT_LEN = 100


manager = ModelManager()
MODEL_NAMES = manager.list_tts_models()

# filter out multi-speaker models and slow wavegrad vocoders
filters = ["vctk", "your_tts", "ek1"]
MODEL_NAMES = [model_name for model_name in MODEL_NAMES if not any(f in model_name for f in filters)]

# reorder models
MODEL_NAMES[0], MODEL_NAMES[1], MODEL_NAMES[2]= MODEL_NAMES[1], MODEL_NAMES[2], MODEL_NAMES[0]
print(MODEL_NAMES)


def tts(text: str, model_name: str, speaker_idx: str=None):
    if len(text) > MAX_TXT_LEN:
        text = text[:MAX_TXT_LEN]
        print(f"Input text was cutoff since it went over the {MAX_TXT_LEN} character limit.")
    print(text, model_name)
    # download model
    model_path, config_path, model_item = manager.download_model(f"tts_models/{model_name}")
    vocoder_name: Optional[str] = model_item["default_vocoder"]
    # download vocoder
    vocoder_path = None
    vocoder_config_path = None
    if vocoder_name is not None:
        vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name)
    # init synthesizer
    synthesizer = Synthesizer(
        model_path, config_path, None, None, vocoder_path, vocoder_config_path,
    )
    # synthesize
    if synthesizer is None:
        raise NameError("model not found")
    wavs = synthesizer.tts(text, speaker_idx)
    # return output
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        synthesizer.save_wav(wavs, fp)
        return fp.name



article= """
Visit us on Coqui.ai and drop a 🌟 to 🔗<a href="https://github.com/coqui-ai/TTS" target="_blank">CoquiTTS</a>.

<br/>

Run CoquiTTS locally for the best result. Check out our 🔗<a href="https://tts.readthedocs.io/en/latest/inference.html">documentation</a>.

```bash
$ pip install TTS
...
$ tts --list_models
...
$ tts --text "Text for TTS" --model_name "<type>/<language>/<dataset>/<model_name>" --out_path folder/to/save/output.wav
```
<img src="https://static.scarf.sh/a.png?x-pxid=1404a024-e647-4406-bb9a-4ade0c931182" />
<br/>

👑 <b> Model contributors</b>

- <a href="https://github.com/nmstoker/" target="_blank">@nmstoker</a>
- <a href="https://github.com/kaiidams/" target="_blank">@kaiidams</a>
- <a href="https://github.com/WeberJulian/" target="_blank">@WeberJulian,</a>
- <a href="https://github.com/Edresson/" target="_blank">@Edresson</a>
- <a href="https://github.com/thorstenMueller/" target="_blank">@thorstenMueller</a>
- <a href="https://github.com/r-dh/" target="_blank">@r-dh</a>
- <a href="https://github.com/kirianguiller/" target="_blank">@kirianguiller</a>
- <a href="https://github.com/robinhad/" target="_blank">@robinhad</a>
- <a href="https://github.com/fkarabiber/" target="_blank">@fkarabiber</a>
- <a href="https://github.com/nicolalandro/" target="_blank">@nicolalandro</a>

👉 Drop a ✨PR✨ on 🐸TTS to share a new model and have it included here.
"""

iface = gr.Interface(
    fn=tts,
    inputs=[
        gr.inputs.Textbox(
            label="Input Text",
            default="This sentence has been generated by a speech synthesis system.",
        ),
        gr.inputs.Radio(
            label="Pick a TTS Model - (language/dataset/model_name)",
            choices=MODEL_NAMES,
        ),
        # gr.inputs.Dropdown(label="Select a speaker", choices=SPEAKERS, default=None)
        # gr.inputs.Audio(source="microphone", label="Record your voice.", type="numpy", label=None, optional=False)
    ],
    outputs=gr.outputs.Audio(label="Output"),
    title="🐸💬 CoquiTTS Demo",
    theme="grass",
    description="🐸💬  Coqui TTS - a deep learning toolkit for Text-to-Speech, battle-tested in research and production.",
    article=article,
    allow_flagging=False,
    flagging_options=['error', 'bad-quality', 'wrong-pronounciation'],
    layout="vertical",
    live=False
)
iface.launch(share=False)