app.py

#!/usr/bin/env python3
#
# Copyright      2022-2023  Xiaomi Corp.        (authors: Fangjun Kuang)
#
# See LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# References:
# https://gradio.app/docs/#dropdown

import logging
import os
import time
import uuid

import gradio as gr
import soundfile as sf

from model import get_pretrained_model, language_to_models

title = "# Equipo 3 - Texto a Voz"

description = """
Este espacio muestra el comó convertir texto a voz con la tecnología Piper.

El proceso de convertir sucede en un CPU con un contenedor docker dado por la plataforma Hugging Face.

Si quiere obtener más información visite los siguientes links:

- <https://github.com/k2-fsa/sherpa-onnx>
- <https://github.com/rhasspy/piper>
- <https://gradio.app/>

Tambien existen aplicaciones android con esta tecnología en el siguiente enlace:

- <https://huggingface.co/csukuangfj/sherpa-onnx-apk/tree/main/tts>

"""

# css style is copied from
# https://huggingface.co/spaces/alphacep/asr/blob/main/app.py#L113
css = """
.result {display:flex;flex-direction:column}
.result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
.result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
.result_item_error {background-color:#ff7070;color:white;align-self:start}
"""


def update_model_dropdown(language: str):
    if language in language_to_models:
        choices = language_to_models[language]
        return gr.Dropdown(
            choices=choices,
            value=choices[0],
            interactive=True,
        )

    raise ValueError(f"Unsupported language: {language}")


def build_html_output(s: str, style: str = "result_item_success"):
    return f"""
    <div class='result'>
        <div class='result_item {style}'>
          {s}
        </div>
    </div>
    """


def process(language: str, repo_id: str, text: str, sid: str, speed: float):
    logging.info(f"Input text: {text}. sid: {sid}, speed: {speed}")
    sid = int(sid)
    tts = get_pretrained_model(repo_id, speed)

    start = time.time()
    audio = tts.generate(text, sid=sid)
    end = time.time()

    if len(audio.samples) == 0:
        raise ValueError(
            "Error in generating audios. Please read previous error messages."
        )

    duration = len(audio.samples) / audio.sample_rate

    elapsed_seconds = end - start
    rtf = elapsed_seconds / duration

    info = f"""
    Duracion del audio  : {duration:.3f} s <br/>
    Tiempo de Procesado: {elapsed_seconds:.3f} s <br/>
    RTF: {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f} <br/>
    """

    logging.info(info)
    logging.info(f"\nrepo_id: {repo_id}\ntext: {text}\nsid: {sid}\nspeed: {speed}")

    filename = str(uuid.uuid4())
    filename = f"{filename}.wav"
    sf.write(
        filename,
        audio.samples,
        samplerate=audio.sample_rate,
        subtype="PCM_16",
    )

    return filename, build_html_output(info)


demo = gr.Blocks(css=css)


with demo:
    gr.Markdown(title)
    language_choices = list(language_to_models.keys())

    with gr.Tabs():
        with gr.TabItem("Por favor ingresa tu texto"):
            input_text = gr.Textbox(
                label="Texto",
                info="Tu texto",
                lines=3,
                placeholder="Por favor ingresa tu texto aquí",
            )

            input_speed = gr.Slider(
                minimum=0.1,
                maximum=10,
                value=1,
                step=0.1,
                label="Velocidad",
            )

            input_button = gr.Button("Convertir")

            output_audio = gr.Audio(label="Salida")

            output_info = gr.HTML(label="Info")

        input_button.click(
            process,
            inputs=[
                gr.Radio(visible=False,value=language_choices[0]),
                gr.Dropdown(visible=False,value=language_to_models[language_choices[0]][0]),
                input_text,
                gr.Textbox(visible=False,value="0"),
                input_speed,
            ],
            outputs=[
                output_audio,
                output_info,
            ],
        )

    gr.Markdown(description)


def download_espeak_ng_data():
    os.system(
        """
    cd /tmp
    wget -qq https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/espeak-ng-data.tar.bz2
    tar xf espeak-ng-data.tar.bz2
    """
    )


if __name__ == "__main__":
    download_espeak_ng_data()
    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

    logging.basicConfig(format=formatter, level=logging.INFO)

    demo.launch()