#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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from funasr import AutoModel
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model = AutoModel(model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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model_revision="master",
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# vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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# vad_model_revision="master",
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# punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
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# punc_model_revision="master",
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# spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
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# spk_model_revision="v2.0.2",
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)
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# example1
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res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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hotword='达摩院 魔搭',
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# return_raw_text=True, # return raw text recognition results splited by space of equal length with timestamp
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# preset_spk_num=2, # preset speaker num for speaker cluster model
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# sentence_timestamp=True, # return sentence level information when spk_model is not given
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)
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print(res)
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'''
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# tensor or numpy as input
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# example2
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import torchaudio
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import os
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wav_file = os.path.join(model.model_path, "example/asr_example.wav")
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input_tensor, sample_rate = torchaudio.load(wav_file)
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input_tensor = input_tensor.mean(0)
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res = model.generate(input=[input_tensor], batch_size_s=300, is_final=True)
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# example3
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import soundfile
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wav_file = os.path.join(model.model_path, "example/asr_example.wav")
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speech, sample_rate = soundfile.read(wav_file)
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res = model.generate(input=[speech], batch_size_s=300, is_final=True)
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'''
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