From c2e4e3c2e9be855277d9f4fa9cd0544892ff829a Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 30 八月 2023 09:57:30 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
egs_modelscope/vad/TEMPLATE/README.md | 16 +++++++++-------
1 files changed, 9 insertions(+), 7 deletions(-)
diff --git a/egs_modelscope/vad/TEMPLATE/README.md b/egs_modelscope/vad/TEMPLATE/README.md
index 503b9bf..35897ca 100644
--- a/egs_modelscope/vad/TEMPLATE/README.md
+++ b/egs_modelscope/vad/TEMPLATE/README.md
@@ -1,7 +1,9 @@
+([绠�浣撲腑鏂嘳(./README_zh.md)|English)
+
# Voice Activity Detection
> **Note**:
-> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetune. Here we take the model of FSMN-VAD as example to demonstrate the usage.
+> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetune. Here we take the model of FSMN-VAD as example to demonstrate the usage.
## Inference
@@ -22,7 +24,7 @@
#### [FSMN-VAD-online model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary)
```python
inference_pipeline = pipeline(
- task=Tasks.auto_speech_recognition,
+ task=Tasks.voice_activity_detection,
model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
)
import soundfile
@@ -46,7 +48,7 @@
### API-reference
#### Define pipeline
- `task`: `Tasks.voice_activity_detection`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU
- `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU
- `output_dir`: `None` (Default), the output path of results if set
@@ -70,7 +72,7 @@
FunASR also offer recipes [egs_modelscope/vad/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/vad/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs.
#### Settings of `infer.sh`
-- `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
+- `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk
- `data_dir`: the dataset dir needs to include `wav.scp`
- `output_dir`: output dir of the recognition results
- `batch_size`: `64` (Default), batch size of inference on gpu
@@ -83,7 +85,7 @@
#### Decode with multi GPUs:
```shell
bash infer.sh \
- --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
+ --model "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
--data_dir "./data/test" \
--output_dir "./results" \
--batch_size 1 \
@@ -93,11 +95,11 @@
#### Decode with multi-thread CPUs:
```shell
bash infer.sh \
- --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
+ --model "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
--data_dir "./data/test" \
--output_dir "./results" \
--gpu_inference false \
- --njob 1
+ --njob 64
```
## Finetune with pipeline
--
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