#!/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 uuid from pathlib import Path import gradio as gr from decode import decode from model import get_pretrained_model, get_vad, 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: - If you want to deploy it locally, please see """ # 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.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"""
{s}
""" def process(language: str, repo_id: str, text: str, sid: str): logging.info(f"Input text: {text}. sid: {sid}") sid = int(sid) tts = get_pretrained_model(repo_id) 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
Processing time: {elapsed_seconds:.3f} s
RTF: {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f}
""" logging.info(info) logging.info(f"\nrepo_id: {repo_id}\ntext: {text}") 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_button = gr.Button("Submit") output_audio = gr.Audio(label="Output") output_info = gr.HTML(label="Info") input_button.click( process, inputs=[ language_radio, model_dropdown, input_text, input_sid, ], outputs=[ output_audio, output_info, ], ) gr.Markdown(description) if __name__ == "__main__": formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" logging.basicConfig(format=formatter, level=logging.INFO) demo.launch()