| | |
|---|
| | | vad_model="fsmn-vad", vad_model_revision="v2.0.2", \ |
|---|
| | | punc_model="ct-punc-c", punc_model_revision="v2.0.2", \ |
|---|
| | | spk_model="cam++", spk_model_revision="v2.0.2") |
|---|
| | | res = model(input=f"{model.model_path}/example/asr_example.wav", |
|---|
| | | res = model.generate(input=f"{model.model_path}/example/asr_example.wav", |
|---|
| | | batch_size=64, |
|---|
| | | hotword='魔搭') |
|---|
| | | print(res) |
|---|
| | |
|---|
| | | for i in range(total_chunk_num): |
|---|
| | | speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] |
|---|
| | | is_final = i == total_chunk_num - 1 |
|---|
| | | res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back) |
|---|
| | | res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back) |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | | |
|---|
| | |
|---|
| | | model = AutoModel(model="fsmn-vad", model_revision="v2.0.2") |
|---|
| | | |
|---|
| | | wav_file = f"{model.model_path}/example/asr_example.wav" |
|---|
| | | res = model(input=wav_file) |
|---|
| | | res = model.generate(input=wav_file) |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | | |
|---|
| | |
|---|
| | | for i in range(total_chunk_num): |
|---|
| | | speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] |
|---|
| | | is_final = i == total_chunk_num - 1 |
|---|
| | | res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size) |
|---|
| | | res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size) |
|---|
| | | if len(res[0]["value"]): |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | |
|---|
| | | |
|---|
| | | model = AutoModel(model="ct-punc", model_revision="v2.0.2") |
|---|
| | | |
|---|
| | | res = model(input="那今天的会就到这里吧 happy new year 明年见") |
|---|
| | | res = model.generate(input="那今天的会就到这里吧 happy new year 明年见") |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | | |
|---|
| | |
|---|
| | | |
|---|
| | | wav_file = f"{model.model_path}/example/asr_example.wav" |
|---|
| | | text_file = f"{model.model_path}/example/text.txt" |
|---|
| | | res = model(input=(wav_file, text_file), data_type=("sound", "text")) |
|---|
| | | res = model.generate(input=(wav_file, text_file), data_type=("sound", "text")) |
|---|
| | | print(res) |
|---|
| | | ``` |
|---|
| | | 更多详细用法（[示例](examples/industrial_data_pretraining)） |
|---|
| | |
|---|
| | | pages={2063--2067}, |
|---|
| | | doi={10.21437/Interspeech.2022-9996} |
|---|
| | | } |
|---|
| | | @article{shi2023seaco, |
|---|
| | | author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang}, |
|---|
| | | title={{SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability}}, |
|---|
| | | year=2023, |
|---|
| | | journal={arXiv preprint arXiv:2308.03266(accepted by ICASSP2024)}, |
|---|
| | | } |
|---|
| | | ``` |
|---|
