Faster-Whisper 实时识别电脑语音转文本

前言

以前做的智能对话软件接的Baidu API，想换成本地的，就搭一套Faster-Whisper吧。
下面是B站视频实时转写的截图

项目

搭建环境

所需要的CUDANN已经装好了，如果装的是12.2应该是包含cuBLAS了
没装的，可以从下面链接下载装一下，文末的参考视频中也有讲解
https://github.com/Purfview/whisper-standalone-win/releases/tag/libs

Ancanda的运行环境去Clone一下之前配好的环境，用之前BertVits的即可

安装Faster-Whisper

输入即可安装

pip install faster-whisper

下载模型

https://huggingface.co/Systran/faster-whisper-large-v3
下载完放到代码旁边就可以了

编写测试代码

# local_files_only=True 表示加载本地模型
# model_size_or_path=path 指定加载模型路径
# device="cuda" 指定使用cuda
# compute_type="int8_float16" 量化为8位
# language="zh" 指定音频语言
# vad_filter=True 开启vad
# vad_parameters=dict(min_silence_duration_ms=1000) 设置vad参数
from faster_whisper import WhisperModel

model_size = "large-v3"
path = r"D:\Project\Python_Project\FasterWhisper\large-v3"

# Run on GPU with FP16
model = WhisperModel(model_size_or_path=path, device="cuda", local_files_only=True)
 
# or run on GPU with INT8
# model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
# model = WhisperModel(model_size, device="cpu", compute_type="int8")

segments, info = model.transcribe("audio.wav", beam_size=5, language="zh", vad_filter=True, vad_parameters=dict(min_silence_duration_ms=1000))

print("Detected language '%s' with probability %f" % (info.language, info.language_probability))

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

运行测试代码

找个音频放入文件夹内，输入python main.py即可运行！
可以看到正确（不太正确）的识别出了音频说了什么。

实时转写脚本

新建一个脚本transper.py
运行即可

此处特别感谢开源项目
https://github.com/MyloBishop/transper

import os
import sys
import time
import wave
import tempfile
import threading

import torch
import pyaudiowpatch as pyaudio
from faster_whisper import WhisperModel as whisper

# A bigger audio buffer gives better accuracy
# but also increases latency in response.
# 表示音频缓冲时间的常量
AUDIO_BUFFER = 5

# 此函数使用 PyAudio 库录制音频，并将其保存为一个临时的 WAV 文件。
# 使用 pyaudio.PyAudio 实例创建一个音频流，通过指定回调函数 callback 来实时写入音频数据到 WAV 文件。
# time.sleep(AUDIO_BUFFER) 会阻塞执行，确保录制足够的音频时间。
# 最后，函数返回保存的 WAV 文件的文件名。
def record_audio(p, device):
    """Record audio from output device and save to temporary WAV file."""
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
        filename = f.name
        wave_file = wave.open(filename, "wb")
        wave_file.setnchannels(device["maxInputChannels"])
        wave_file.setsampwidth(pyaudio.get_sample_size(pyaudio.paInt16))
        wave_file.setframerate(int(device["defaultSampleRate"]))

        def callback(in_data, frame_count, time_info, status):
            """Write frames and return PA flag"""
            wave_file.writeframes(in_data)
            return (in_data, pyaudio.paContinue)

        stream = p.open(
            format=pyaudio.paInt16,
            channels=device["maxInputChannels"],
            rate=int(device["defaultSampleRate"]),
            frames_per_buffer=pyaudio.get_sample_size(pyaudio.paInt16),
            input=True,
            input_device_index=device["index"],
            stream_callback=callback,
        )

        try:
            time.sleep(AUDIO_BUFFER)  # Blocking execution while playing
        finally:
            stream.stop_stream()
            stream.close()
            wave_file.close()
            # print(f"{filename} saved.")
    return filename

# 此函数使用 Whisper 模型对录制的音频进行转录，并输出转录结果。
def whisper_audio(filename, model):
    """Transcribe audio buffer and display."""
    # segments, info = model.transcribe(filename, beam_size=5, task="translate", language="zh", vad_filter=True, vad_parameters=dict(min_silence_duration_ms=1000))
    segments, info = model.transcribe(filename, beam_size=5, language="zh", vad_filter=True, vad_parameters=dict(min_silence_duration_ms=1000))
    os.remove(filename)
    # print(f"{filename} removed.")
    for segment in segments:
        # print(f"[{segment.start:.2f} -> {segment.end:.2f}] {segment.text.strip()}")
        print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

# main 函数是整个脚本的主控制函数。
# 加载 Whisper 模型，选择合适的计算设备（GPU 或 CPU）。
# 获取默认的 WASAPI 输出设备信息，并选择默认的扬声器（输出设备）。
# 使用 PyAudio 开始录制音频，并通过多线程运行 whisper_audio 函数进行音频转录。
def main():
    """Load model record audio and transcribe from default output device."""
    print("Loading model...")
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"Using {device} device.")
    # model = whisper("large-v3", device=device, compute_type="float16")
    model = whisper("large-v3", device=device, local_files_only=True)

    print("Model loaded.")

    with pyaudio.PyAudio() as pya:
        # Create PyAudio instance via context manager.
        try:
            # Get default WASAPI info
            wasapi_info = pya.get_host_api_info_by_type(pyaudio.paWASAPI)
        except OSError:
            print("Looks like WASAPI is not available on the system. Exiting...")
            sys.exit()

        # Get default WASAPI speakers
        default_speakers = pya.get_device_info_by_index(
            wasapi_info["defaultOutputDevice"]
        )

        if not default_speakers["isLoopbackDevice"]:
            for loopback in pya.get_loopback_device_info_generator():
                # Try to find loopback device with same name(and [Loopback suffix]).
                # Unfortunately, this is the most adequate way at the moment.
                if default_speakers["name"] in loopback["name"]:
                    default_speakers = loopback
                    break
            else:
                print(
                    """
                    Default loopback output device not found.
                    Run `python -m pyaudiowpatch` to check available devices.
                    Exiting...
                    """
                )
                sys.exit()

        print(
            f"Recording from: {default_speakers['name']} ({default_speakers['index']})\n"
        )

        while True:
            filename = record_audio(pya, default_speakers)
            thread = threading.Thread(target=whisper_audio, args=(filename, model))
            thread.start()

main()

参考

faster-whisper
MyloBishop/transper
基于faster_whisper的实时语音识别
基于faster whisper实现实时语音识别项目语音转文本python编程实现