File size: 5,987 Bytes
9cf08e9 df9fb03 9cf08e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | import os
import re
import json
import tempfile
import logging
import subprocess
import soundfile as sf
from typing import Optional, List
from fastapi import FastAPI, HTTPException, Body, UploadFile, File
from fastapi.responses import JSONResponse
import numpy as np
from contextlib import asynccontextmanager
from dotenv import load_dotenv
load_dotenv()
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)
# 全局 ASR 实例
asr_engine = None
class SherpaASREngine:
"""sherpa-onnx-offline 命令行引擎封装"""
def __init__(
self,
model_dir: str = None,
model_file: str = None,
tokens_file: str = None,
sherpa_bin: str = None,
vad: str = None,
provider: str = "axera",
):
base = model_dir or os.getenv("SHERPA_MODEL_DIR", os.path.dirname(os.path.abspath(__file__)))
self.model_file = model_file or os.getenv("SHERPA_MODEL_FILE", os.path.join(base, "ax650", "model-10-seconds.axmodel"))
self.tokens_file = tokens_file or os.path.join(base, "tokens.txt")
self.sherpa_bin = sherpa_bin or os.path.join(
base,
os.getenv("SHERPA_BIN_DIR", "sherpa-onnx-v1.12.20-axera-ax650-linux-aarch64-shared"),
"bin",
"sherpa-onnx-offline",
)
self.provider = provider or os.getenv("SHERPA_PROVIDER", "axera")
# self.vad = vad or os.getenv("vad-model", "/root/huangjie/AXERA-TECH/SenseVoice/silero_vad.onnx")
if os.path.exists(self.sherpa_bin):
os.chmod(self.sherpa_bin, 0o755)
def run(self, audio_path: str) -> dict:
"""执行识别命令，返回解析后的 JSON 结果"""
cmd = [
self.sherpa_bin,
# f"--silero-vad-model={self.vad}",
f"--sense-voice-model={self.model_file}",
f"--tokens={self.tokens_file}",
f"--provider={self.provider}",
audio_path,
]
result = subprocess.run(cmd, capture_output=True, text=True, timeout=120)
if result.returncode != 0:
logger.error(f"sherpa-onnx failed: {result.stderr}")
raise RuntimeError(f"sherpa-onnx ASR failed: {result.stderr}")
print("result: ", result)
# 解析输出中的 JSON 行
for line in reversed(result.stderr.strip().splitlines()):
line = line.strip()
if line.startswith("{"):
# text=line.json().get("text", "")
# lang=line.json().get("lang", "")
print("lang: ", line)
return json.loads(line)
return {"text": "", "lang": "", "timestamps": []}
def clean_text(text: str) -> str:
"""清理文本中的特殊标记"""
text = re.sub(r'<\|[^|]*\|>', '', text)
text = re.sub(r'\s+', ' ', text).strip()
return text
@asynccontextmanager
async def lifespan(app: FastAPI):
global asr_engine
logger.info("Loading Sherpa-ONNX ASR engine...")
try:
asr_engine = SherpaASREngine()
logger.info("Sherpa-ONNX ASR engine loaded successfully")
except Exception as e:
logger.error(f"Failed to load Sherpa-ONNX ASR engine: {str(e)}")
raise
yield
app = FastAPI(title="Sherpa-ONNX ASR Server", description="SenseVoice ASR via sherpa-onnx-offline", lifespan=lifespan)
@app.post("/asr", summary="Recognize speech from raw audio data")
async def recognize_speech(
audio_data: List[float] = Body(..., embed=True, description="Audio data as list of floats"),
sample_rate: Optional[int] = Body(16000, description="Audio sample rate in Hz"),
):
"""接收 numpy 数组格式的音频数据并返回识别结果"""
if asr_engine is None:
raise HTTPException(status_code=503, detail="ASR engine not loaded")
try:
np_audio = np.array(audio_data, dtype=np.float32)
if np_audio.ndim != 1 or len(np_audio) == 0:
raise HTTPException(status_code=400, detail="Audio data must be a non-empty 1D array")
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
tmp_path = tmp.name
sf.write(tmp_path, np_audio, sample_rate)
try:
result = asr_engine.run(tmp_path)
result["text"] = clean_text(result.get("text", ""))
return JSONResponse(content=result)
finally:
try:
os.remove(tmp_path)
except Exception:
pass
except HTTPException:
raise
except Exception as e:
logger.error(f"Recognition error: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
@app.post("/asr/file", summary="Recognize speech from uploaded audio file")
async def recognize_file(file: UploadFile = File(..., description="Audio file (wav, mp3, etc.)")):
"""接收音频文件并返回识别结果"""
if asr_engine is None:
raise HTTPException(status_code=503, detail="ASR engine not loaded")
try:
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
tmp_path = tmp.name
content = await file.read()
tmp.write(content)
try:
result = asr_engine.run(tmp_path)
result["text"] = clean_text(result.get("text", ""))
return JSONResponse(content=result)
finally:
try:
os.remove(tmp_path)
except Exception:
pass
except HTTPException:
raise
except Exception as e:
logger.error(f"Recognition error: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
@app.get("/health")
async def health_check():
return {"status": "ok", "model_loaded": asr_engine is not None}
if __name__ == "__main__":
import uvicorn
port = int(os.getenv("SHERPA_ASR_API_PORT", 8006))
uvicorn.run(app, host="0.0.0.0", port=port)
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