From 570901174ec4c30305ce4e5ac78696259a68ad1e Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 21 四月 2023 00:45:33 +0800
Subject: [PATCH] docs
---
docs/modescope_pipeline/vad_pipeline.md | 44 +++++++++++++++++++++-----------------------
egs_modelscope/asr/TEMPLATE/README.md | 8 ++++----
2 files changed, 25 insertions(+), 27 deletions(-)
diff --git a/docs/modescope_pipeline/vad_pipeline.md b/docs/modescope_pipeline/vad_pipeline.md
index 9d9b77a..ca8a5ee 100644
--- a/docs/modescope_pipeline/vad_pipeline.md
+++ b/docs/modescope_pipeline/vad_pipeline.md
@@ -42,22 +42,23 @@
Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/236)
+
#### API-reference
-##### define pipeline
+##### 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
-- `ngpu`: 1 (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
-- `ncpu`: 1 (Defalut), sets the number of threads used for intraop parallelism on CPU
-- `output_dir`: None (Defalut), the output path of results if set
-- `batch_size`: 1 (Defalut), batch size when decoding
-##### infer pipeline
+- `ngpu`: `1` (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
+- `ncpu`: `1` (Defalut), sets the number of threads used for intraop parallelism on CPU
+- `output_dir`: `None` (Defalut), the output path of results if set
+- `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, `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
+ ```text
asr_example1 ./audios/asr_example1.wav
asr_example2 ./audios/asr_example2.wav
```
@@ -66,41 +67,38 @@
- `output_dir`: None (Defalut), the output path of results if set
### Inference with multi-thread CPUs or multi GPUs
-FunASR also offer recipes [infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE//infer.sh) to decode with multi-thread CPUs, or multi GPUs.
+FunASR also offer recipes [infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs.
- Setting parameters in `infer.sh`
- - <strong>model:</strong> # model name on ModelScope
- - <strong>data_dir:</strong> # the dataset dir needs to include `${data_dir}/wav.scp`. If `${data_dir}/text` is also exists, CER will be computed
- - <strong>output_dir:</strong> # result dir
- - <strong>batch_size:</strong> # batchsize of inference
- - <strong>gpu_inference:</strong> # whether to perform gpu decoding, set false for cpu decoding
- - <strong>gpuid_list:</strong> # set gpus, e.g., gpuid_list="0,1"
- - <strong>njob:</strong> # the number of jobs for CPU decoding, if `gpu_inference`=false, use CPU decoding, please set `njob`
+ - `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
+ - `data_dir`: the dataset dir needs to include `wav.scp`. If `${data_dir}/text` is also exists, CER will be computed
+ - `output_dir`: output dir of the recognition results
+ - `batch_size`: `64` (Default), batch size of inference on gpu
+ - `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference
+ - `gpuid_list`: `0,1` (Default), which gpu_ids are used to infer
+ - `njob`: only used for CPU inference (`gpu_inference`=`false`), `64` (Default), the number of jobs for CPU decoding
+ - `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models
+ - `checkpoint_name`: only used for infer finetuned models, `valid.cer_ctc.ave.pb` (Default), which checkpoint is used to infer
- Decode with multi GPUs:
```shell
bash infer.sh \
- --model "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
+ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
--data_dir "./data/test" \
--output_dir "./results" \
+ --batch_size 64 \
--gpu_inference true \
--gpuid_list "0,1"
```
- Decode with multi-thread CPUs:
```shell
bash infer.sh \
- --model "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
+ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
--data_dir "./data/test" \
--output_dir "./results" \
--gpu_inference false \
--njob 64
```
-
-- Results
-
-The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set.
-
-If you decode the SpeechIO test sets, you can use textnorm with `stage`=3, and `DETAILS.txt`, `RESULTS.txt` record the results and CER after text normalization.
## Finetune with pipeline
diff --git a/egs_modelscope/asr/TEMPLATE/README.md b/egs_modelscope/asr/TEMPLATE/README.md
index a739dbe..7e81f87 100644
--- a/egs_modelscope/asr/TEMPLATE/README.md
+++ b/egs_modelscope/asr/TEMPLATE/README.md
@@ -62,10 +62,10 @@
##### 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
-- `ngpu`: 1 (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
-- `ncpu`: 1 (Defalut), sets the number of threads used for intraop parallelism on CPU
-- `output_dir`: None (Defalut), the output path of results if set
-- `batch_size`: 1 (Defalut), batch size when decoding
+- `ngpu`: `1` (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
+- `ncpu`: `1` (Defalut), sets the number of threads used for intraop parallelism on CPU
+- `output_dir`: `None` (Defalut), the output path of results if set
+- `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,
--
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