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 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

def generate_audio(tts_text, prompt_text, prompt_wav_upload, prompt_wav_record, seed, select_which):
    if select_which == "上傳檔案" and prompt_wav_upload is not None:
        prompt_wav = prompt_wav_upload
    elif select_which == "麥克風" and prompt_wav_record is not None:
        prompt_wav = prompt_wav_record
    else:
        prompt_wav = None
    # 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)


def generate_text(prompt_wav_upload, prompt_wav_record, select_which):
    # Determine which input to use based on the selection in select_which
    if select_which == "上傳檔案" and prompt_wav_upload is not None:
        prompt_wav = prompt_wav_upload
        LAST_UPLOADED = "upload"
    elif select_which == "麥克風" and prompt_wav_record is not None:
        prompt_wav = prompt_wav_record
        LAST_UPLOADED = "record"
    else:
        prompt_wav = None
        LAST_UPLOADED = None
    print(select_which)
    # Process with ASR pipeline
    if prompt_wav:
        results = asr_pipeline(prompt_wav)
        return results['text']
    return "No valid input detected."

# LAST_UPLOADED = ""
# def switch_selected(select_which):
#     # Check the file type (assuming WAV file)
#     if select_which == "上傳檔案" and prompt_wav_upload is not None:
#         prompt_wav = prompt_wav_upload
#         LAST_UPLOADED = "upload"
#     elif select_which == "麥克風" and prompt_wav_record is not None:
#         prompt_wav = prompt_wav_record
#     return "麥克風"

def demo_get_audio(tts_text):
    sample_wav = 'sample.wav'
    speech, sample_rate = torchaudio.load(sample_wav)
    
    return sample_rate, speech
def main():
    with gr.Blocks(title="BreezyVoice 語音合成系統", theme="default") as demo:
        # Title and About section at the top
        gr.Markdown("# BreezyVoice 語音合成系統")
        gr.Markdown(
            """### 僅需5秒語音樣本，就可輸出擬真人聲。"""
        )
        with gr.Row():
            gr.Image(value="https://huggingface.co/spaces/Splend1dchan/BreezyVoice-Playground/resolve/main/flowchart.png", interactive=False, scale=3) 
        gr.Markdown(
            """#### 此沙盒使用 Huggingface CPU，請預期大於200 秒的推理時間，您可以考慮以下方法加速：
            1. **強烈建議**複製這個 Space（Duplicate this space），以分散流量！
            2. 複製至本地GPU執行（請參考[指南](https://huggingface.co/docs/hub/en/spaces-overview)）或使用[kaggle](https://www.kaggle.com/code/a24998667/breezyvoice-playground)
            3. 複製至本地CPU執行（請參考[指南](https://huggingface.co/docs/hub/en/spaces-overview)）

            為了加快推理速度，g2pw注音標註並未被啟動。

            免責聲明：此沙盒在一次性容器地端執行，關閉後檔案將遭到刪除。此沙盒不屬於聯發創新基地，聯發創新基地無法獲得任何使用者輸入。"""
        )

        # 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_upload = gr.Audio(
                sources='upload',
                type='filepath',
                label='選擇prompt音訊檔案（確保取樣率不低於16khz）'
            )
            prompt_wav_record = gr.Audio(
                sources='microphone',
                type='filepath',
                label='錄製prompt音訊檔案'
            )
            
            with gr.Blocks():
                select_which = gr.Radio(["上傳檔案", "麥克風"], label="音訊來源", interactive=True )
            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"
                )

            # Automatic speech recognition when either prompt audio input changes
            def a(X):
                return "上傳檔案"
            prompt_wav_upload.change(
                fn=a,#lambda file: "上傳檔案",
                inputs=[prompt_wav_upload],
                outputs=select_which
            )

            
            
            

            prompt_wav_record.change(
                fn=lambda recording: "麥克風",
                inputs=[prompt_wav_record],
                outputs=select_which
            )

            select_which.change(
                fn=generate_text,
                inputs=[prompt_wav_upload, prompt_wav_record, select_which],
                outputs=prompt_text
            )
            # select_which.change(
            #     fn=switch_selected,
            #     inputs=[select_which],
            #     outputs= None
            # )
            # 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="你好，歡迎光臨"
            )


            # 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_upload, prompt_wav_record, seed, select_which],
                outputs=audio_output
            )

        demo.queue(max_size=10, default_concurrency_limit=1)
        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()