app.py

# Copyright (c) 2025 MediaTek Reserch Inc (authors: Chan-Jan Hsu)
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Liu Yue)
#
# 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.
import spaces

import os
import sys
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append('{}/third_party/Matcha-TTS'.format(ROOT_DIR))

import argparse
import gradio as gr
import numpy as np
import torch
torch.set_num_threads(1)
import torchaudio
import random
import librosa
from transformers import pipeline
import subprocess
from scipy.signal import resample

import logging
logging.getLogger('matplotlib').setLevel(logging.WARNING)

from cosyvoice.cli.cosyvoice import CosyVoice
from cosyvoice.utils.file_utils import load_wav, speed_change

#logging.basicConfig(level=logging.DEBUG,
#                    format='%(asctime)s %(levelname)s %(message)s')

def generate_seed():
    seed = random.randint(1, 100000000)
    return {
        "__type__": "update",
        "value": seed
    }

def set_all_random_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

max_val = 0.8
def postprocess(speech, top_db=60, hop_length=220, win_length=440):
    speech, _ = librosa.effects.trim(
        speech, top_db=top_db,
        frame_length=win_length,
        hop_length=hop_length
    )
    if speech.abs().max() > max_val:
        speech = speech / speech.abs().max() * max_val
    speech = torch.concat([speech, torch.zeros(1, int(target_sr * 0.2))], dim=1)
    return speech

@spaces.GPU
def generate_audio(tts_text, prompt_text, prompt_wav, seed):
    # if instruct mode, please make sure that model is iic/CosyVoice-300M-Instruct and not cross_lingual mode

    prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr))
    set_all_random_seed(seed)
    output = cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k)
    speed_factor = 1
    if speed_factor != 1.0:
        #try:
            #audio_data, sample_rate = speed_change(output["tts_speech"], target_sr, str(speed_factor))
            #audio_data = audio_data.numpy().flatten()
        new_length = int(len(output['tts_speech']) / speed_factor)
        audio_data = resample(output['tts_speech'], new_length)
        # except Exception as e:
        #     print(f"Failed to change speed of audio: \n{e}")
    else:
        audio_data = output['tts_speech'].numpy().flatten()

    return (target_sr, audio_data)


@spaces.GPU
def generate_text(prompt_wav):
    if prompt_wav:
        results = asr_pipeline(prompt_wav)
        return results['text']
    return "No valid input detected."

def main():
    with gr.Blocks(title="BreezyVoice 語音合成系統", theme="default") as demo:
        gr.Markdown(
            """# BreezyVoice 語音合成系統
            
            #### Runs on Huggingface Zero GPU (A100)

            為了加快推理速度，g2pw 注音標註並未被啟動。"""
        )

        # All content arranged in a single column
        with gr.Column():
            # Configuration Section

            # Grouping prompt audio inputs and auto speech recognition in one block using Markdown
            gr.Markdown("### 步驟 1. 音訊樣本輸入 & 音訊樣本文本輸入")
            gr.Markdown("選擇 prompt 音訊檔案或錄製 prompt 音訊 (5~15秒)，並手動校對自動產生的音訊樣本文本。")
            prompt_wav = gr.Audio(
                type='filepath',
                label='選擇 prompt 音訊檔案（確保取樣率不低於 16khz）或錄製 prompt 音訊'
            )

            with gr.Blocks():
                prompt_text = gr.Textbox(
                    label="音訊樣本文本輸入(此欄位應與音檔內容完全相同)",
                    lines=2,
                    placeholder="音訊樣本文本"
                )

            prompt_wav.input(
                fn=generate_text,
                inputs=[prompt_wav],
                outputs=prompt_text
            )

            gr.Examples(
                examples=[
                    ["examples/commonvoice-example-1.mp3", "明月幾時有，去問氣象局"],
                    ["examples/commonvoice-example-2.mp3", "雲林縣斗六市與林內鄉交界"],
                    ["examples/commonvoice-example-3.mp3", "法律應保障所有的人獲得相同的發展結果"]
                ],
                inputs=[prompt_wav, prompt_text],
                label="範例"
            )

            # Input Section: Synthesis Text

            gr.Markdown("### 步驟 2.合成文本輸入")
            tts_text = gr.Textbox(
                label="輸入想要合成的文本",
                lines=2,
                placeholder="請輸入想要合成的文本...",
                value="我今天忙了一整天，現在好想睡覺喔 QQ"
            )


            # Output Section
            gr.Markdown("### 步驟 3. 合成音訊")
            # Generation button for audio synthesis (triggered manually)

            with gr.Accordion("進階設定", open=False):
                seed = gr.Number(value=0, label="隨機推理種子")
                #seed_button = gr.Button("隨機")
                seed_button = gr.Button(value="\U0001F3B2生成隨機推理種子\U0001F3B2")
                speed_factor = 1
                # speed_factor = gr.Slider(
                #     minimum=0.25,
                #     maximum=4,
                #     step=0.05,
                #     label="語速",
                #     value=1.0,
                #     interactive=True
                # )

            generate_button = gr.Button("生成音訊")
            audio_output = gr.Audio(label="合成音訊")

            # Set up callbacks for seed generation and audio synthesis
            seed_button.click(fn=generate_seed, inputs=[], outputs=seed)
            generate_button.click(
                fn=generate_audio,
                inputs=[tts_text, prompt_text, prompt_wav, seed],
                outputs=audio_output
            )

        demo.launch()

if __name__ == '__main__':
    cosyvoice = CosyVoice('Splend1dchan/BreezyVoice')
    asr_pipeline = pipeline(
        "automatic-speech-recognition",
        model="openai/whisper-tiny",
        tokenizer="openai/whisper-tiny",
        device=0  # Use GPU (if available); set to -1 for CPU
    )
    sft_spk = cosyvoice.list_avaliable_spks()
    prompt_sr, target_sr = 16000, 22050
    default_data = np.zeros(target_sr)
    main()