mirror of
https://github.com/modelscope/FunASR
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702ec03ad8
* add hotword for deploy_tools * Support wfst decoder and contextual biasing (#1039) * Support wfst decoder and contextual biasing * Turn on fstbin compilation --------- Co-authored-by: gongbo.gb <gongbo.gb@alibaba-inc.com> * mv funasr/runtime runtime * Fix crash caused by OOV in hotwords list * funasr infer * funasr infer * funasr infer * funasr infer * funasr infer * fix some bugs about fst hotword; support wfst for websocket server and clients; mv runtime out of funasr; modify relative docs * del onnxruntime/include/gflags * update tensor.h * update run_server.sh * update deploy tools * update deploy tools * update websocket-server * update funasr-wss-server * Remove self loop propagation * Update websocket_protocol_zh.md * Update websocket_protocol_zh.md * update hotword protocol * author zhaomingwork: change hotwords for h5 and java * update hotword protocol * catch exception for json_fst_hws * update hotword on message * update onnx benchmark for ngram&hotword * update docs * update funasr-wss-serve * add NONE for LM_DIR * update docs * update run_server.sh * add whats-new * modify whats-new * update whats-new * update whats-new * Support decoder option for beam searching * update benchmark_onnx_cpp * Support decoder option for websocket * fix bug of CompileHotwordEmbedding * update html client * update docs --------- Co-authored-by: gongbo.gb <35997837+aibulamusi@users.noreply.github.com> Co-authored-by: gongbo.gb <gongbo.gb@alibaba-inc.com> Co-authored-by: 游雁 <zhifu.gzf@alibaba-inc.com>
55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
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import time
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import sys
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import librosa
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from funasr.utils.types import str2bool
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('--model_dir', type=str, required=True)
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parser.add_argument('--backend', type=str, default='onnx', help='["onnx", "torch"]')
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parser.add_argument('--wav_file', type=str, default=None, help='amp fallback number')
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parser.add_argument('--quantize', type=str2bool, default=False, help='quantized model')
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parser.add_argument('--intra_op_num_threads', type=int, default=1, help='intra_op_num_threads for onnx')
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args = parser.parse_args()
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from funasr.runtime.python.libtorch.funasr_torch import Paraformer
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if args.backend == "onnx":
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from funasr.runtime.python.onnxruntime.funasr_onnx import Paraformer
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model = Paraformer(args.model_dir, batch_size=1, quantize=args.quantize, intra_op_num_threads=args.intra_op_num_threads)
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wav_file_f = open(args.wav_file, 'r')
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wav_files = wav_file_f.readlines()
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# warm-up
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total = 0.0
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num = 30
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wav_path = wav_files[0].split("\t")[1].strip() if "\t" in wav_files[0] else wav_files[0].split(" ")[1].strip()
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for i in range(num):
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beg_time = time.time()
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result = model(wav_path)
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end_time = time.time()
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duration = end_time-beg_time
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total += duration
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print(result)
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print("num: {}, time, {}, avg: {}, rtf: {}".format(len(wav_path), duration, total/(i+1), (total/(i+1))/5.53))
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# infer time
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beg_time = time.time()
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for i, wav_path_i in enumerate(wav_files):
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wav_path = wav_path_i.split("\t")[1].strip() if "\t" in wav_path_i else wav_path_i.split(" ")[1].strip()
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result = model(wav_path)
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end_time = time.time()
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duration = (end_time-beg_time)*1000
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print("total_time_comput_ms: {}".format(int(duration)))
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duration_time = 0.0
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for i, wav_path_i in enumerate(wav_files):
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wav_path = wav_path_i.split("\t")[1].strip() if "\t" in wav_path_i else wav_path_i.split(" ")[1].strip()
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waveform, _ = librosa.load(wav_path, sr=16000)
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duration_time += len(waveform)/16.0
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print("total_time_wav_ms: {}".format(int(duration_time)))
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print("total_rtf: {:.5}".format(duration/duration_time)) |