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import subprocess
import numpy as np
from typing import List
import sherpa_onnx
from tools.utils import must_have_value
class SenseVoiceRecognitionResult:
def __init__(self, text, begin_time, end_time):
self.text = text
self.begin_time = begin_time
self.end_time = end_time
def __str__(self):
return f"{self.text} {self.begin_time} {self.end_time}"
class SenseVoiceRecognitionService:
def __init__(self):
super().__init__()
self.model_path = "sensevoice/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/model.onnx"
must_have_value(self.model_path, "请设置 SenseVoice 模型路径")
self.tokens_path = "sensevoice/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/tokens.txt"
must_have_value(self.tokens_path, "请设置 SenseVoice tokens 路径")
def process(self, audioFile, language) -> List[SenseVoiceRecognitionResult]:
result_list = []
# 创建 SenseVoice 识别器
recognizer = sherpa_onnx.OfflineRecognizer.from_sense_voice(
model=self.model_path,
tokens=self.tokens_path,
num_threads=2, # 可以根据需要调整线程数
use_itn=True,
debug=False,
)
# 使用 ffmpeg 将音频文件转换为 16kHz 16bit 单声道 PCM 格式
ffmpeg_cmd = [
"ffmpeg",
"-i", audioFile,
"-f", "s16le",
"-acodec", "pcm_s16le",
"-ac", "1",
"-ar", "16000",
"-",
]
process = subprocess.Popen(ffmpeg_cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
data = process.stdout.read()
# 将音频数据转换为 float32 格式
samples = np.frombuffer(data, dtype=np.int16)
samples = samples.astype(np.float32) / 32768
# 创建识别流并处理音频数据
stream = recognizer.create_stream()
stream.accept_waveform(16000, samples)
recognizer.decode_stream(stream)
# 获取识别结果
result = stream.result.text
# 假设整个音频的起止时间为 0 到音频长度
result_list.append(SenseVoiceRecognitionResult(result, 0, len(samples) / 16000))
return result_list |