From f77c5803f4d61099e572be8d877b1c4a4d6087cd Mon Sep 17 00:00:00 2001
From: yhliang <68215459+yhliang-aslp@users.noreply.github.com>
Date: 星期三, 10 五月 2023 12:02:06 +0800
Subject: [PATCH] Merge pull request #485 from alibaba-damo-academy/main
---
egs_modelscope/tp/TEMPLATE/README.md | 42 +++++++++++++++++++++---------------------
1 files changed, 21 insertions(+), 21 deletions(-)
diff --git a/egs_modelscope/tp/TEMPLATE/README.md b/egs_modelscope/tp/TEMPLATE/README.md
index 2678a7f..7cc8508 100644
--- a/egs_modelscope/tp/TEMPLATE/README.md
+++ b/egs_modelscope/tp/TEMPLATE/README.md
@@ -8,12 +8,12 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
-inference_pipline = pipeline(
+inference_pipeline = pipeline(
task=Tasks.speech_timestamp,
model='damo/speech_timestamp_prediction-v1-16k-offline',
output_dir=None)
-rec_result = inference_pipline(
+rec_result = inference_pipeline(
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_timestamps.wav',
text_in='涓� 涓� 涓� 澶� 骞� 娲� 鍥� 瀹� 涓� 浠� 涔� 璺� 鍒� 瑗� 澶� 骞� 娲� 鏉� 浜� 鍛�',)
print(rec_result)
@@ -23,15 +23,15 @@
-#### API-reference
-##### Define pipeline
+### API-reference
+#### Define pipeline
- `task`: `Tasks.speech_timestamp`
-- `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
- `batch_size`: `1` (Default), batch size when decoding
-##### Infer pipeline
+#### Infer pipeline
- `audio_in`: the input speech to predict, which could be:
- wav_path, `e.g.`: asr_example.wav (wav in local or url),
- wav.scp, kaldi style wav list (`wav_id wav_path`), `e.g.`:
@@ -59,37 +59,37 @@
```
### Inference with multi-thread CPUs or multi GPUs
-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.
+FunASR also offer recipes [egs_modelscope/tp/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/tp/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs.
-- Setting parameters in `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
- - `data_dir`: the dataset dir **must** include `wav.scp` and `text.scp`
- - `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
+#### Settings of `infer.sh`
+- `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 **must** include `wav.scp` and `text.txt`
+- `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:
+#### Decode with multi GPUs:
```shell
bash infer.sh \
--model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
--data_dir "./data/test" \
--output_dir "./results" \
- --batch_size 64 \
+ --batch_size 1 \
--gpu_inference true \
--gpuid_list "0,1"
```
-- Decode with multi-thread CPUs:
+#### Decode with multi-thread CPUs:
```shell
bash infer.sh \
--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
+ --njob 1
```
## Finetune with pipeline
--
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