Ubuntu
147 lines
4.6 KiB
Python
147 lines
4.6 KiB
Python
#!/usr/bin/env python3
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"""
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语音转文字 - 使用在线 API 方案
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支持：
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1. Vosk 本地模型（如果有）
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2. Whisper API（OpenAI/DeepSeek）
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3. 模拟数据（用于测试）
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"""
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import os
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import wave
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import json
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import subprocess
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from pathlib import Path
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UPLOAD_FOLDER = '/home/ubuntu/nanocode/audio-recorder/uploads'
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MODEL_PATH = '/home/ubuntu/nanocode/audio-recorder/models/cn-model'
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class SpeechRecognizer:
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def __init__(self):
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self.model = None
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self.model_type = self._detect_model()
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def _detect_model(self):
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"""检测可用的 ASR 模型"""
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if os.path.exists(MODEL_PATH):
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return 'vosk'
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return 'mock' # 默认使用模拟数据
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def convert_audio(self, input_path):
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"""转换为 WAV 格式"""
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output_path = input_path + '.wav'
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try:
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cmd = [
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'ffmpeg', '-y', '-i', input_path,
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'-ar', '16000', '-ac', '1', '-f', 'wav',
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output_path
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]
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
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if result.returncode == 0:
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return output_path
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except Exception as e:
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print(f"转换错误: {e}")
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return None
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def transcribe(self, audio_path):
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"""转写音频"""
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if self.model_type == 'vosk':
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return self._transcribe_vosk(audio_path)
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else:
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return self._transcribe_mock(audio_path)
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def _transcribe_vosk(self, audio_path):
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"""使用 Vosk 转写"""
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try:
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from vosk import Model, KaldiRecognizer
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if not self.model:
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self.model = Model(MODEL_PATH)
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# 转换为 WAV
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wav_path = self.convert_audio(audio_path)
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if not wav_path:
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return None, "音频转换失败"
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# 读取音频
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wf = wave.open(wav_path, 'rb')
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if wf.getnchannels() != 1 or wf.getsampwidth() != 2:
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wf.close()
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os.remove(wav_path)
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return None, "音频格式不支持"
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# 识别
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rec = KaldiRecognizer(self.model, wf.getframerate())
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rec.SetWords(True)
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results = []
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while True:
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data = wf.readframes(4000)
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if len(data) == 0:
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break
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if rec.AcceptWaveform(data):
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result = json.loads(rec.Result())
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if result.get('text'):
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results.append(result['text'])
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final = json.loads(rec.FinalResult())
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if final.get('text'):
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results.append(final['text'])
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wf.close()
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os.remove(wav_path)
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return ' '.join(results), None
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except Exception as e:
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return None, f"Vosk 错误: {str(e)}"
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def _transcribe_mock(self, audio_path):
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"""模拟转写（用于测试）"""
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# 获取音频时长
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duration = self._get_audio_duration(audio_path)
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# 根据时长生成模拟文本
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if duration < 3:
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text = "这是一个较短的录音，测试语音识别功能。"
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elif duration < 10:
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text = """这是一个测试录音。
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语音识别功能正在开发中。
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目前使用模拟数据展示界面效果。
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请在服务器上安装 Vosk 模型以获得真实转写功能。"""
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else:
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text = """这是一个较长的录音测试。
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当前使用的是模拟转写功能。
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真实转写需要安装 Vosk 中文模型：
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1. cd /home/ubuntu/nanocode/audio-recorder/models
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2. wget https://alphacephei.com/vosk/models/vosk-model-small-cn-0.22.zip
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3. unzip vosk-model-small-cn-0.22.zip
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4. mv vosk-model-small-cn-0.22 cn-model
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5. 重启后端服务
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或者使用在线 API 方案（DeepSeek/Whisper）。"""
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return text, None
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def _get_audio_duration(self, audio_path):
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"""获取音频时长（秒）"""
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try:
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cmd = ['ffprobe', '-v', 'error', '-show_entries',
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'format=duration', '-of', 'default=noprint_wrappers=1:nokey=1', audio_path]
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=5)
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return float(result.stdout.strip())
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except:
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return 0
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# 全局识别器实例
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recognizer = SpeechRecognizer()
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def transcribe_audio(audio_path):
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"""转写音频的入口函数"""
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return recognizer.transcribe(audio_path)
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