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https://github.com/modelscope/FunASR
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* sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * bugfix * update with main (#1631) * update seaco finetune * v1.0.24 --------- Co-authored-by: 维石 <shixian.shi@alibaba-inc.com> * sensevoice * sensevoice * sensevoice * update with main (#1638) * update seaco finetune * v1.0.24 * update rwkv template --------- Co-authored-by: 维石 <shixian.shi@alibaba-inc.com> * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * whisper * whisper * update style * update style --------- Co-authored-by: 维石 <shixian.shi@alibaba-inc.com>
32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import soundfile
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from funasr_onnx.paraformer_online_bin import Paraformer
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from pathlib import Path
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model_dir = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
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wav_path = ["{}/.cache/modelscope/hub/{}/example/asr_example.wav".format(Path.home(), model_dir)]
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chunk_size = [5, 10, 5]
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model = Paraformer(
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model_dir, batch_size=1, quantize=True, chunk_size=chunk_size, intra_op_num_threads=4
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) # only support batch_size = 1
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##online asr
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speech, sample_rate = soundfile.read(wav_path)
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speech_length = speech.shape[0]
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sample_offset = 0
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step = chunk_size[1] * 960
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param_dict = {"cache": dict()}
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final_result = ""
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for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)):
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if sample_offset + step >= speech_length - 1:
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step = speech_length - sample_offset
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is_final = True
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else:
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is_final = False
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param_dict["is_final"] = is_final
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rec_result = model(audio_in=speech[sample_offset : sample_offset + step], param_dict=param_dict)
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if len(rec_result) > 0:
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final_result += rec_result[0]["preds"][0]
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print(rec_result)
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print(final_result)
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