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@@ -17,7 +17,7 @@ rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyu
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print(rec_result)
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```
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-#### API-docs
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+#### API-reference
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##### define pipeline
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- `task`: `Tasks.auto_speech_recognition`
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- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
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@@ -27,11 +27,17 @@ print(rec_result)
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- `batch_size`: 1 (Defalut), batch size when decoding
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##### infer pipeline
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- `audio_in`: the input to decode, which could be:
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- - wav_path, `e.g.`: asr_example.wav,
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- - pcm_path,
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- - audio bytes stream
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- - audio sample point
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- - wav.scp
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+ - wav_path, `e.g.`: asr_example.wav,
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+ - pcm_path, `e.g.`: asr_example.pcm,
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+ - audio bytes stream, `e.g.`: bytes data from a microphone
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+ - audio sample point，`e.g.`: `audio, rate = soundfile.read("asr_example_zh.wav")`, the dtype is numpy.ndarray or torch.Tensor
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+ - wav.scp, kaldi style wav list (`wav_id \t wav_path``), `e.g.`:
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+ ```cat wav.scp
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+ asr_example1 ./audios/asr_example1.wav
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+ asr_example2 ./audios/asr_example2.wav
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+ ```
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+ In this case of `wav.scp` input, `output_dir` must be set to save the output results
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+- `audio_fs`: audio sampling rate, only set when audio_in is pcm audio
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#### Inference with you data
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游雁