From cfe22850e2a62071fca0fbdc15bb6e95ca555490 Mon Sep 17 00:00:00 2001
From: yufan-aslp <379840315@qq.com>
Date: 星期二, 25 四月 2023 15:42:03 +0800
Subject: [PATCH] update mfcc infer.sh
---
egs_modelscope/asr/TEMPLATE/README.md | 48 ++++++++++++++++++++++++++++++++----------------
1 files changed, 32 insertions(+), 16 deletions(-)
diff --git a/egs_modelscope/asr/TEMPLATE/README.md b/egs_modelscope/asr/TEMPLATE/README.md
index a5d7d6e..94b47ec 100644
--- a/egs_modelscope/asr/TEMPLATE/README.md
+++ b/egs_modelscope/asr/TEMPLATE/README.md
@@ -1,7 +1,7 @@
# Speech Recognition
> **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 finetine. Here we take typic model as example to demonstrate the usage.
+> 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 finetine. Here we take the typic models as examples to demonstrate the usage.
## Inference
@@ -53,19 +53,35 @@
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)
```
-The decoding mode of `fast` and `normal`
+The decoding mode of `fast` and `normal` is fake streaming, which could be used for evaluating of recognition accuracy.
Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/151)
#### [RNN-T-online model]()
Undo
+
+#### [MFCCA Model](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary)
+For more model detailes, please refer to [docs](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary)
+```python
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950',
+ model_revision='v3.0.0'
+)
+
+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)
+```
#### 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
-- `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` (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
- `audio_in`: the input to decode, which could be:
- wav_path, `e.g.`: asr_example.wav,
@@ -79,19 +95,19 @@
```
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
-- `output_dir`: None (Defalut), the output path of results if set
+- `output_dir`: None (Default), 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.
- Setting parameters in `infer.sh`
- - `model`: model name on ModelScope
- - `data_dir`: the dataset dir needs to include `${data_dir}/wav.scp`. If `${data_dir}/text` is also exists, CER will be computed
- - `output_dir`: result dir
- - `batch_size`: batchsize of inference
- - `gpu_inference`: whether to perform gpu decoding, set false for cpu decoding
- - `gpuid_list`: set gpus, e.g., `gpuid_list`="0,1"
- - `njob`: 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
@@ -186,7 +202,7 @@
```
## Inference with your finetuned model
-- Setting parameters in [infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) is the same with [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/egs_modelscope/asr/TEMPLATE#inference-with-multi-thread-cpus-or-multi-gpus)
+- Setting parameters in [infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) is the same with [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/egs_modelscope/asr/TEMPLATE#inference-with-multi-thread-cpus-or-multi-gpus), `model` is the model name from modelscope, which you finetuned.
- Decode with multi GPUs:
```shell
@@ -210,4 +226,4 @@
--njob 64 \
--checkpoint_dir "./checkpoint" \
--checkpoint_name "valid.cer_ctc.ave.pb"
-```
\ No newline at end of file
+```
--
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