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#!/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 = "# Next-gen Kaldi: Text-to-speech (TTS)"
description = """
This space shows how to convert text to speech with Next-gen Kaldi.
It is running on CPU within a docker container provided by Hugging Face.
See more information by visiting the following links:
- <https://github.com/k2-fsa/sherpa-onnx>
If you want to deploy it locally, please see
<https://k2-fsa.github.io/sherpa/>
If you want to use Android APKs, please see
<https://k2-fsa.github.io/sherpa/onnx/tts/apk.html>
If you want to use Android text-to-speech engine APKs, please see
<https://k2-fsa.github.io/sherpa/onnx/tts/apk-engine.html>
If you want to download an all-in-one exe for Windows, please see
<https://github.com/k2-fsa/sherpa-onnx/releases/tag/tts-models>
"""
# 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}
"""
examples = [
["Min-nan (闽南话)", "csukuangfj/vits-mms-nan", "ài piaǸ chiah ē iaN̂", 0, 1.0],
["Thai", "csukuangfj/vits-mms-tha", "ฉันรักคุณ", 0, 1.0],
]
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"""
Wave duration : {duration:.3f} s <br/>
Processing time: {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())
language_radio = gr.Radio(
label="Language",
choices=language_choices,
value=language_choices[0],
)
model_dropdown = gr.Dropdown(
choices=language_to_models[language_choices[0]],
label="Select a model",
value=language_to_models[language_choices[0]][0],
)
language_radio.change(
update_model_dropdown,
inputs=language_radio,
outputs=model_dropdown,
)
with gr.Tabs():
with gr.TabItem("Please input your text"):
input_text = gr.Textbox(
label="Input text",
info="Your text",
lines=3,
placeholder="Please input your text here",
)
input_sid = gr.Textbox(
label="Speaker ID",
info="Speaker ID",
lines=1,
max_lines=1,
value="0",
placeholder="Speaker ID. Valid only for mult-speaker model",
)
input_speed = gr.Slider(
minimum=0.1,
maximum=10,
value=1,
step=0.1,
label="Speed (larger->faster; smaller->slower)",
)
input_button = gr.Button("Submit")
output_audio = gr.Audio(label="Output")
output_info = gr.HTML(label="Info")
gr.Examples(
examples=examples,
fn=process,
inputs=[
language_radio,
model_dropdown,
input_text,
input_sid,
input_speed,
],
outputs=[
output_audio,
output_info,
],
)
input_button.click(
process,
inputs=[
language_radio,
model_dropdown,
input_text,
input_sid,
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()
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