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| | | ``` |
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| | | Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word. |
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| | | |
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| | | <details><summary>More Examples</summary> |
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| | | |
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| | | ### Voice Activity Detection (Non-Streaming) |
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| | | ```python |
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| | | from funasr import AutoModel |
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| | |
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| | | res = model.generate(input=(wav_file, text_file), data_type=("sound", "text")) |
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| | | print(res) |
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| | | ``` |
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| | | |
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| | | |
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| | | ### Speech Emotion Recognition |
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| | | ```python |
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| | | from funasr import AutoModel |
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| | | |
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| | | model = AutoModel(model="emotion2vec_plus_large") |
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| | | |
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| | | wav_file = f"{model.model_path}/example/test.wav" |
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| | | |
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| | | res = model.generate(wav_file, output_dir="./outputs", granularity="utterance", extract_embedding=False) |
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| | | print(res) |
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| | | ``` |
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| | | |
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| | | More usages ref to [docs](docs/tutorial/README_zh.md), |
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| | | more examples ref to [demo](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining) |
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| | | |
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| | | </details> |
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| | | |
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| | | ## Export ONNX |
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| | | |
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