520 lines
22 KiB
Python
520 lines
22 KiB
Python
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import os
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import sys
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import time
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import uuid
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import datetime
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import tempfile
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import soundfile as sf
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import sherpa_onnx
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import traceback
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from fastapi import FastAPI, File, UploadFile, HTTPException, BackgroundTasks, Form
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os.makedirs("./input", exist_ok=True)
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status = "Running"
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recognizer = None
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device = ""
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model_type = ""
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app = FastAPI()
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CUSTOM_DEVICE = os.getenv("CUSTOM_DEVICE", "")
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# 根据名称判断模型类型，比较杂，一共种类的自定义类型包括(针对OfflineRecognizer)
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# moonshine
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# fire_red
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# dolphin_ctc
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# paraformer
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# telespeech_ctc
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# whisper
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# sensevoice
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# zipformer_ctc
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# transducer
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# nemo_ctc
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# nemo_canary
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# wenet_ctc
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# 针对OnlineRecognizer只有 zipformer_ctc transducer paraformer nemo_ctc wenet_ctc 四种
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def get_asr_model_type(model_name):
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# 根据名称判断模型类型以及需要检测的语种任务
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# nemo_ctc, nemo_canary, moonshine 目前sherpa-onnx没有中文模型，执行英文ASR任务，其余模型执行中文ASR
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# 所有nemo模型(nemo_ctc, nemo_canary以及transuducer中的nemo模型)均无中文模型
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# 英文模型也并非全部大类都支持
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# 特殊规则
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# zipformer带ctc的才属于zipformer_ctc那一类，否则属于transducer类
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# nemo也是带上ctc或者canary才属于单独类别，否则属于transducer类
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# conformer均为transducer类,但是得在nemo之后判断
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# wenet 由于同时wenetspeech为数据集名称，各种类型都有可能，这个逻辑需放在后面
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model_type = "unknown"
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model_name_lower = model_name.lower()
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if "tdnn" in model_name_lower:
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model_type = "tdnn" # tdnn类别不适用，目前仅有一个模型只能识别希伯来语中的yes/no两种词语
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elif "moonshine" in model_name_lower:
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model_type = "moonshine"
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elif "fire-red" in model_name_lower:
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model_type = "fire_red"
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elif "dolphin" in model_name_lower:
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model_type = "dolphin_ctc"
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elif "paraformer" in model_name_lower:
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model_type = "paraformer"
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elif "telespeech" in model_name_lower:
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model_type = "telespeech_ctc"
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elif "whisper" in model_name_lower:
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model_type = "whisper"
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elif "sense-voice" in model_name_lower:
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model_type = "sensevoice"
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elif "zipformer" in model_name_lower:
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if "ctc" in model_name_lower:
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model_type = "zipformer_ctc"
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else:
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model_type = "transducer"
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elif "nemo" in model_name_lower:
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if "ctc" in model_name_lower:
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model_type = "nemo_ctc"
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elif "canary" in model_name_lower:
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model_type = "nemo_canary"
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else:
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model_type = "transducer"
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elif "conformer" in model_name_lower or "lstm" in model_name_lower:
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model_type = "transducer"
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elif "wenet" in model_name_lower:
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model_type = "wenet_ctc"
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else:
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model_type = "unknown"
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return model_type
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@app.on_event("startup")
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def load_model():
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global status, recognizer, device, model_type
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config = app.state.config
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use_gpu = config.get("use_gpu", True)
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model_dir = config.get("model_dir", "/model")
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_model_type = config.get("model_type", None)
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_model_name = config.get("model_name", None)
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warmup = config.get("warmup", False)
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isOffline = config.get("offline_model", True)
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num_threads = config.get("num_threads", 2)
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device = "cpu"
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if use_gpu:
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if CUSTOM_DEVICE.startswith("mlu"):
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device = "mlu:0"
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elif CUSTOM_DEVICE.startswith("ascend"):
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device = "npu:0"
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else:
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device = "cuda:0"
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# sherpa-onnx类型繁杂，当用户清楚的时候可提供model_type参数，抑或是提供完整的模型名称也行
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# 因为挂载进入镜像的时候镜像内的文件路径不一定包含了模型名称
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if _model_type:
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model_type = _model_type
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elif _model_name:
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model_type = get_asr_model_type(_model_name)
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else:
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print("model_name and model_type both not provided, start guessing using model_dir", flush=True)
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model_name = os.path.basename(model_dir)
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model_type = get_asr_model_type(model_name)
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print(">> Startup config:")
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print(" model_dir =", model_dir, flush=True)
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print(" model_type =", model_type, flush=True)
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print(" use_gpu =", use_gpu, flush=True)
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print(" warmup =", warmup, flush=True)
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print(" isOffline =", isOffline, flush=True)
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print(" num_threads =", num_threads, flush=True)
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try:
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recognizer = None
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provider = "cuda" if use_gpu else "cpu"
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file_list = os.listdir(model_dir)
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# 目录内的模型文件可能会有多套（例如量化和不带量化版），选取大小最大的那一套
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if model_type == "whisper":
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encoder_list, decoder_list = [], []
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tokens = ""
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for file in file_list:
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if "encode" in file and file.endswith(".onnx"):
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encoder_list.append(file)
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elif "decode" in file and file.endswith(".onnx"):
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decoder_list.append(file)
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elif "token" in file and file.endswith(".txt"):
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tokens = file
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encoder = sorted(encoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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decoder = sorted(decoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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recognizer = sherpa_onnx.OfflineRecognizer.from_whisper(
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encoder=model_dir + "/" + encoder,
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decoder=model_dir + "/" + decoder,
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tokens=model_dir + "/" + tokens,
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language="zh",
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debug=False,
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provider=provider,
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num_threads=num_threads
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)
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elif model_type == "sensevoice":
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model_list = []
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tokens = ""
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for file in file_list:
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if file.endswith(".onnx"):
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model_list.append(file)
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elif file.endswith(".txt") and "token" in file:
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tokens = file
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model = sorted(model_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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recognizer = sherpa_onnx.OfflineRecognizer.from_sense_voice(
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model=model_dir + "/" + model,
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tokens=model_dir + "/" + tokens,
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debug=False,
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use_itn=True,
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language="zh",
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provider=provider,
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num_threads=num_threads
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)
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elif model_type == "paraformer":
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model_list = []
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for file in file_list:
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if file.endswith(".onnx"):
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model_list.append(file)
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elif file.endswith(".txt") and "token" in file:
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tokens = file
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model = sorted(model_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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if isOffline:
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recognizer = sherpa_onnx.OfflineRecognizer.from_paraformer(
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paraformer=model_dir + "/" + model,
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tokens=model_dir + "/" + tokens,
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debug=False,
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provider=provider,
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num_threads=num_threads
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)
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else:
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recognizer = sherpa_onnx.OnlineRecognizer.from_paraformer(
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paraformer=model_dir + "/" + model,
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tokens=model_dir + "/" + tokens,
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debug=False,
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provider=provider,
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num_threads=num_threads
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)
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elif model_type == "zipformer_ctc":
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model_list = []
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for file in file_list:
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if file.endswith(".onnx"):
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model_list.append(file)
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elif file.endswith(".txt") and "token" in file:
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tokens = file
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model = sorted(model_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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if isOffline:
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recognizer = sherpa_onnx.OfflineRecognizer.from_zipformer_ctc(
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model=model_dir + "/" + model,
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tokens=model_dir + "/" + tokens,
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debug=False,
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provider=provider,
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num_threads=num_threads
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)
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else:
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recognizer = sherpa_onnx.OnlineRecognizer.from_zipformer2_ctc(
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model=model_dir + "/" + model,
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tokens=model_dir + "/" + tokens,
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debug=False,
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provider=provider,
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num_threads=num_threads
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)
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elif model_type == "telespeech_ctc":
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model_list = []
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for file in file_list:
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if file.endswith(".onnx"):
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model_list.append(file)
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elif file.endswith(".txt") and "token" in file:
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tokens = file
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model = sorted(model_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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recognizer = sherpa_onnx.OfflineRecognizer.from_telespeech_ctc(
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model=model_dir + "/" + model,
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tokens=model_dir + "/" + tokens,
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debug=False,
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provider=provider,
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num_threads=num_threads
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)
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elif model_type == "fire_red":
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encoder_list, decoder_list = [], []
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tokens = ""
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for file in file_list:
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if "encode" in file and file.endswith(".onnx"):
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encoder_list.append(file)
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elif "decode" in file and file.endswith(".onnx"):
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decoder_list.append(file)
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elif "token" in file and file.endswith(".txt"):
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tokens = file
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encoder = sorted(encoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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decoder = sorted(decoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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recognizer = sherpa_onnx.OfflineRecognizer.from_fire_red_asr(
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encoder=model_dir + "/" + encoder,
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decoder=model_dir + "/" + decoder,
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tokens=model_dir + "/" + tokens,
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debug=False,
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provider=provider,
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num_threads=num_threads
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)
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elif model_type == "wenet_ctc":
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model_list = []
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for file in file_list:
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if file.endswith(".onnx"):
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model_list.append(file)
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elif file.endswith(".txt") and "token" in file:
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tokens = file
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model = sorted(model_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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if isOffline:
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recognizer = sherpa_onnx.OfflineRecognizer.from_wenet_ctc(
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model=model_dir + "/" + model,
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tokens=model_dir + "/" + tokens,
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debug=False,
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provider=provider,
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num_threads=num_threads
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)
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else:
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recognizer = sherpa_onnx.OnlineRecognizer.from_wenet_ctc(
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model=model_dir + "/" + model,
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tokens=model_dir + "/" + tokens,
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debug=False,
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provider=provider,
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num_threads=num_threads
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)
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elif model_type == "dolphin_ctc":
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model_list = []
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for file in file_list:
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if file.endswith(".onnx"):
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model_list.append(file)
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elif file.endswith(".txt") and "token" in file:
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tokens = file
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model = sorted(model_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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recognizer = sherpa_onnx.OfflineRecognizer.from_dolphin_ctc(
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model=model_dir + "/" + model,
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tokens=model_dir + "/" + tokens,
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debug=False,
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provider=provider,
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num_threads=num_threads
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)
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elif model_type == "transducer":
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encoder_list, decoder_list, joiner_list = [], [], []
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tokens = ""
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for file in file_list:
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if "encode" in file and file.endswith(".onnx"):
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encoder_list.append(file)
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elif "decode" in file and file.endswith(".onnx"):
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decoder_list.append(file)
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elif "joiner" in file and file.endswith(".onnx"):
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joiner_list.append(file)
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elif "token" in file and file.endswith(".txt"):
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tokens = file
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encoder = sorted(encoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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decoder = sorted(decoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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joiner = sorted(joiner_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
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# 特殊情况，zipformer,conformer都是icefall导出，默认类型即可，nemo-transducer需要专门区分
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transducer_type = "nemo_transducer" if "nemo" in model_name.lower() else "transducer"
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if isOffline:
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recognizer = sherpa_onnx.OfflineRecognizer.from_transducer(
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encoder=model_dir + "/" + encoder,
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decoder=model_dir + "/" + decoder,
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joiner=model_dir + "/" + joiner,
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|
|
tokens=model_dir + "/" + tokens,
|
|||
|
|
model_type=transducer_type,
|
|||
|
|
debug=False,
|
|||
|
|
provider=provider,
|
|||
|
|
num_threads=num_threads
|
|||
|
|
)
|
|||
|
|
else:
|
|||
|
|
recognizer = sherpa_onnx.OnlineRecognizer.from_transducer(
|
|||
|
|
encoder=model_dir + "/" + encoder,
|
|||
|
|
decoder=model_dir + "/" + decoder,
|
|||
|
|
joiner=model_dir + "/" + joiner,
|
|||
|
|
tokens=model_dir + "/" + tokens,
|
|||
|
|
debug=False,
|
|||
|
|
provider=provider,
|
|||
|
|
num_threads=num_threads
|
|||
|
|
)
|
|||
|
|
elif model_type == "nemo_ctc":
|
|||
|
|
model_list = []
|
|||
|
|
for file in file_list:
|
|||
|
|
if file.endswith(".onnx"):
|
|||
|
|
model_list.append(file)
|
|||
|
|
elif file.endswith(".txt") and "token" in file:
|
|||
|
|
tokens = file
|
|||
|
|
model = sorted(model_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
|
|||
|
|
if isOffline:
|
|||
|
|
recognizer = sherpa_onnx.OfflineRecognizer.from_nemo_ctc(
|
|||
|
|
model=model_dir + "/" + model,
|
|||
|
|
tokens=model_dir + "/" + tokens,
|
|||
|
|
debug=False,
|
|||
|
|
provider=provider,
|
|||
|
|
num_threads=num_threads
|
|||
|
|
)
|
|||
|
|
else:
|
|||
|
|
recognizer = sherpa_onnx.OnlineRecognizer.from_nemo_ctc(
|
|||
|
|
model=model_dir + "/" + model,
|
|||
|
|
tokens=model_dir + "/" + tokens,
|
|||
|
|
debug=False,
|
|||
|
|
provider=provider,
|
|||
|
|
num_threads=num_threads
|
|||
|
|
)
|
|||
|
|
elif model_type == "nemo_canary":
|
|||
|
|
encoder_list, decoder_list = [], []
|
|||
|
|
tokens = ""
|
|||
|
|
for file in file_list:
|
|||
|
|
if "encode" in file and file.endswith(".onnx"):
|
|||
|
|
encoder_list.append(file)
|
|||
|
|
elif "decode" in file and file.endswith(".onnx"):
|
|||
|
|
decoder_list.append(file)
|
|||
|
|
elif "token" in file and file.endswith(".txt"):
|
|||
|
|
tokens = file
|
|||
|
|
encoder = sorted(encoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
|
|||
|
|
decoder = sorted(decoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
|
|||
|
|
recognizer = sherpa_onnx.OfflineRecognizer.from_nemo_canary(
|
|||
|
|
encoder=model_dir + "/" + encoder,
|
|||
|
|
decoder=model_dir + "/" + decoder,
|
|||
|
|
tokens=model_dir + "/" + tokens,
|
|||
|
|
debug=False,
|
|||
|
|
provider=provider,
|
|||
|
|
num_threads=num_threads
|
|||
|
|
)
|
|||
|
|
elif model_type == "moonshine":
|
|||
|
|
preprocessor_list, encoder_list, cached_decoder_list, uncached_decoder_list = [], [], [], []
|
|||
|
|
tokens = ""
|
|||
|
|
for file in file_list:
|
|||
|
|
if "preprocess" in file and file.endswith(".onnx"):
|
|||
|
|
preprocessor_list.append(file)
|
|||
|
|
elif "encode" in file and file.endswith(".onnx"):
|
|||
|
|
encoder_list.append(file)
|
|||
|
|
elif "uncached_decode" in file and file.endswith(".onnx"):
|
|||
|
|
uncached_decoder_list.append(file)
|
|||
|
|
elif "cached_decode" in file and file.endswith(".onnx"):
|
|||
|
|
cached_decoder_list.append(file)
|
|||
|
|
elif "token" in file and file.endswith(".txt"):
|
|||
|
|
tokens = file
|
|||
|
|
preprocessor = sorted(preprocessor_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
|
|||
|
|
encoder = sorted(encoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
|
|||
|
|
cached_decoder = sorted(cached_decoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
|
|||
|
|
uncached_decoder = sorted(uncached_decoder_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
|
|||
|
|
recognizer = sherpa_onnx.OfflineRecognizer.from_moonshine(
|
|||
|
|
preprocessor=model_dir + "/" + preprocessor,
|
|||
|
|
encoder=model_dir + "/" + encoder,
|
|||
|
|
cached_decoder=model_dir + "/" + cached_decoder,
|
|||
|
|
uncached_decoder=model_dir + "/" + uncached_decoder,
|
|||
|
|
tokens=model_dir + "/" + tokens,
|
|||
|
|
debug=False,
|
|||
|
|
provider=provider,
|
|||
|
|
num_threads=num_threads
|
|||
|
|
)
|
|||
|
|
elif model_type == "tdnn_ctc":
|
|||
|
|
model_list = []
|
|||
|
|
for file in file_list:
|
|||
|
|
if file.endswith(".onnx"):
|
|||
|
|
model_list.append(file)
|
|||
|
|
elif file.endswith(".txt") and "token" in file:
|
|||
|
|
tokens = file
|
|||
|
|
model = sorted(model_list, key=lambda x: os.path.getsize(model_dir + "/" + x), reverse=True)[0]
|
|||
|
|
recognizer = sherpa_onnx.OfflineRecognizer.from_tdnn_ctc(
|
|||
|
|
model=model_dir + "/" + model,
|
|||
|
|
tokens=model_dir + "/" + tokens,
|
|||
|
|
debug=False,
|
|||
|
|
provider=provider,
|
|||
|
|
num_threads=num_threads
|
|||
|
|
)
|
|||
|
|
else:
|
|||
|
|
raise RuntimeError("Cannot recognize model_type")
|
|||
|
|
except Exception as e:
|
|||
|
|
raise RuntimeError(f"Failed to initial cuda model: {e}")
|
|||
|
|
|
|||
|
|
if warmup:
|
|||
|
|
print("Start warmup...", flush=True)
|
|||
|
|
stream = recognizer.create_stream()
|
|||
|
|
audio, sample_rate = sf.read("warmup.wav", dtype="float32", always_2d=True)
|
|||
|
|
stream.accept_waveform(sample_rate, audio)
|
|||
|
|
recognizer.decode_stream(stream)
|
|||
|
|
print("warmup complete.", flush=True)
|
|||
|
|
|
|||
|
|
status = "Success"
|
|||
|
|
|
|||
|
|
def test_sherpa(wavefile):
|
|||
|
|
isOffline = app.state.config.get("offline_model", True)
|
|||
|
|
audio, sample_rate = sf.read(wavefile, dtype="float32", always_2d=True)
|
|||
|
|
audio = audio[:, 0]
|
|||
|
|
generated_text = ""
|
|||
|
|
|
|||
|
|
start_t = datetime.datetime.now()
|
|||
|
|
if isOffline:
|
|||
|
|
# OfflineRecognizer非流式模型推理
|
|||
|
|
if model_type in ["sensevoice"]:
|
|||
|
|
stream = recognizer.create_stream()
|
|||
|
|
stream.accept_waveform(sample_rate, audio)
|
|||
|
|
recognizer.decode_stream(stream)
|
|||
|
|
generated_text = stream.result.text
|
|||
|
|
else:
|
|||
|
|
# offline-asr model 大多对长音频支持不佳，模型训练音频不长以及导出onnx结构中对一些中间态维度可能有上限
|
|||
|
|
# 哪怕原版CPU推理中间可能都会崩溃，采取小段切分形式测试
|
|||
|
|
start_index = 0
|
|||
|
|
internal = int(sample_rate * 29)
|
|||
|
|
while start_index < len(audio):
|
|||
|
|
stream = recognizer.create_stream()
|
|||
|
|
stream.accept_waveform(sample_rate, audio[start_index:start_index + internal])
|
|||
|
|
recognizer.decode_stream(stream)
|
|||
|
|
generated_text += stream.result.text
|
|||
|
|
start_index += internal
|
|||
|
|
else:
|
|||
|
|
# OnlineRecognizer流式模型推理,统一每一次只投喂2s音频数据
|
|||
|
|
stream = recognizer.create_stream()
|
|||
|
|
start_index = 0
|
|||
|
|
chunk_size = int(sample_rate * 2)
|
|||
|
|
|
|||
|
|
while start_index < len(audio):
|
|||
|
|
chunk = audio[start_index:start_index + chunk_size]
|
|||
|
|
stream.accept_waveform(sample_rate, chunk)
|
|||
|
|
|
|||
|
|
while recognizer.is_ready(stream):
|
|||
|
|
recognizer.decode_stream(stream)
|
|||
|
|
# mid_text = recognizer.get_result(stream)
|
|||
|
|
# print("partial result: " + mid_text, flush=True)
|
|||
|
|
start_index += chunk_size
|
|||
|
|
|
|||
|
|
while recognizer.is_ready(stream):
|
|||
|
|
recognizer.decode_stream(stream)
|
|||
|
|
generated_text = recognizer.get_result(stream)
|
|||
|
|
|
|||
|
|
end_t = datetime.datetime.now()
|
|||
|
|
elapsed_seconds = (end_t - start_t).total_seconds()
|
|||
|
|
duration = audio.shape[-1] / sample_rate
|
|||
|
|
rtf = elapsed_seconds / duration
|
|||
|
|
|
|||
|
|
print("Text:", generated_text, flush=True)
|
|||
|
|
print(f"Audio duration:\t{duration:.3f} s", flush=True)
|
|||
|
|
print(f"Elapsed:\t{elapsed_seconds:.3f} s", flush=True)
|
|||
|
|
print(f"RTF = {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f}", flush=True)
|
|||
|
|
|
|||
|
|
return generated_text
|
|||
|
|
|
|||
|
|
@app.get("/health")
|
|||
|
|
def health():
|
|||
|
|
|
|||
|
|
if status=="Running":
|
|||
|
|
return {
|
|||
|
|
"status":"loading model"
|
|||
|
|
}
|
|||
|
|
ret = {
|
|||
|
|
"status": "ok" if status == "Success" else "failed",
|
|||
|
|
}
|
|||
|
|
return ret
|
|||
|
|
|
|||
|
|
@app.post("/transduce")
|
|||
|
|
def transduce(
|
|||
|
|
audio: UploadFile = File(...),
|
|||
|
|
lang: str = Form("zh"),
|
|||
|
|
background_tasks: BackgroundTasks = None
|
|||
|
|
):
|
|||
|
|
try:
|
|||
|
|
file_path = f"./input/{uuid.uuid4()}.wav"
|
|||
|
|
with open(file_path, "wb") as f:
|
|||
|
|
f.write(audio.file.read())
|
|||
|
|
background_tasks.add_task(os.remove, file_path)
|
|||
|
|
generated_text = test_sherpa(file_path)
|
|||
|
|
|
|||
|
|
return {"generated_text": generated_text}
|
|||
|
|
except Exception:
|
|||
|
|
raise HTTPException(status_code=500, detail=f"Processing failed: \n{traceback.format_exc()}")
|
|||
|
|
|
|||
|
|
# if __name__ == "__main__":
|
|||
|
|
|
|||
|
|
# uvicorn.run("fastapi_sherpa:app", host="0.0.0.0", port=1111, workers=1)
|