From aa54e65287ff99ab364bc9893bff79900c8c4cc8 Mon Sep 17 00:00:00 2001
From: Lizerui9926 <110582652+Lizerui9926@users.noreply.github.com>
Date: 星期四, 20 四月 2023 11:38:45 +0800
Subject: [PATCH] Merge pull request #386 from alibaba-damo-academy/dev_sx
---
docs/modescope_pipeline/asr_pipeline.md | 51 +++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 41 insertions(+), 10 deletions(-)
diff --git a/docs/modescope_pipeline/asr_pipeline.md b/docs/modescope_pipeline/asr_pipeline.md
index f5bbe9f..ee4b3ff 100644
--- a/docs/modescope_pipeline/asr_pipeline.md
+++ b/docs/modescope_pipeline/asr_pipeline.md
@@ -1,9 +1,12 @@
# Speech Recognition
+> **Note**:
+> The modelscope pipeline supports all the models in [model zoo] to inference and finetine. Here we take model of Paraformer and Paraformer-online as example to demonstrate the usage.
+
## Inference
### Quick start
-#### Paraformer model
+#### [Paraformer model](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary)
```python
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
@@ -16,8 +19,29 @@
rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
print(rec_result)
```
+#### [Paraformer-online model](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary)
+```python
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
+ )
+import soundfile
+speech, sample_rate = soundfile.read("example/asr_example.wav")
-#### API-docs
+param_dict = {"cache": dict(), "is_final": False}
+chunk_stride = 7680# 480ms
+# first chunk, 480ms
+speech_chunk = speech[0:chunk_stride]
+rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
+# next chunk, 480ms
+speech_chunk = speech[chunk_stride:chunk_stride+chunk_stride]
+rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
+
+print(rec_result)
+```
+Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/241)
+
+#### 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
@@ -27,17 +51,24 @@
- `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锛宍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
-#### Inference with multi-threads on CPU
+### Inference with you data
-#### Inference with multi GPU
+### Inference with multi-threads on CPU
+
+### Inference with multi GPU
## Finetune with pipeline
--
Gitblit v1.9.1