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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.update(choices=choices, value=choices[0]) | |
| 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() | |