| | |
|---|
| | | print(rec_result) |
|---|
| | | ``` |
|---|
| | | |
|---|
| | | #### API-docs |
|---|
| | | #### API-reference |
|---|
| | | ##### define pipeline |
|---|
| | | - `task`: `Tasks.auto_speech_recognition` |
|---|
| | | - `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 |
|---|
| | |
|---|
| | | - `batch_size`: 1 (Defalut), batch size when decoding |
|---|
| | | ##### infer pipeline |
|---|
| | | - `audio_in`: the input to decode, which could be: |
|---|
| | | - wav_path, `e.g.`: asr_example.wav, |
|---|
| | | - pcm_path, |
|---|
| | | - audio bytes stream |
|---|
| | | - audio sample point |
|---|
| | | - wav.scp |
|---|
| | | - wav_path, `e.g.`: asr_example.wav, |
|---|
| | | - pcm_path, `e.g.`: asr_example.pcm, |
|---|
| | | - audio bytes stream, `e.g.`: bytes data from a microphone |
|---|
| | | - audio sample point，`e.g.`: `audio, rate = soundfile.read("asr_example_zh.wav")`, the dtype is numpy.ndarray or torch.Tensor |
|---|
| | | - wav.scp, kaldi style wav list (`wav_id \t wav_path``), `e.g.`: |
|---|
| | | ```cat wav.scp |
|---|
| | | asr_example1 ./audios/asr_example1.wav |
|---|
| | | asr_example2 ./audios/asr_example2.wav |
|---|
| | | ``` |
|---|
| | | In this case of `wav.scp` input, `output_dir` must be set to save the output results |
|---|
| | | - `audio_fs`: audio sampling rate, only set when audio_in is pcm audio |
|---|
| | | |
|---|
| | | #### Inference with you data |
|---|
| | | |
|---|
