From 26f70e6b328e8df32e2ac1d8ba3a38c7cf7bdf29 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期日, 03 三月 2024 20:02:44 +0800
Subject: [PATCH] update docs
---
README.md | 28 ++++++++++++++++++----------
1 files changed, 18 insertions(+), 10 deletions(-)
diff --git a/README.md b/README.md
index 454adc9..295cdc9 100644
--- a/README.md
+++ b/README.md
@@ -66,7 +66,7 @@
## Model Zoo
FunASR has open-sourced a large number of pre-trained models on industrial data. You are free to use, copy, modify, and share FunASR models under the [Model License Agreement](./MODEL_LICENSE). Below are some representative models, for more models please refer to the [Model Zoo]().
-(Note: 馃 represents the Huggingface model zoo link, 猸� represents the ModelScope model zoo link)
+(Note: 猸� represents the ModelScope model zoo link, 馃 represents the Huggingface model zoo link)
| Model Name | Task Details | Training Data | Parameters |
@@ -79,6 +79,7 @@
| fsmn-vad <br> ( [猸怾(https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) [馃](https://huggingface.co/funasr/fsmn-vad) ) | voice activity detection | 5000 hours, Mandarin and English | 0.4M |
| fa-zh <br> ( [猸怾(https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [馃](https://huggingface.co/funasr/fa-zh) ) | timestamp prediction | 5000 hours, Mandarin | 38M |
| cam++ <br> ( [猸怾(https://modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) [馃](https://huggingface.co/funasr/campplus) ) | speaker verification/diarization | 5000 hours | 7.2M |
+| whisper-large-v2 <br> ([猸怾(https://www.modelscope.cn/models/iic/speech_whisper-large_asr_multilingual/summary) [馃]() ) | speech recognition, with timestamps, non-streaming | multilingual | 1G |
@@ -105,10 +106,8 @@
from funasr import AutoModel
# paraformer-zh is a multi-functional asr model
# use vad, punc, spk or not as you need
-model = AutoModel(model="paraformer-zh", model_revision="v2.0.4",
- vad_model="fsmn-vad", vad_model_revision="v2.0.4",
- punc_model="ct-punc-c", punc_model_revision="v2.0.4",
- # spk_model="cam++", spk_model_revision="v2.0.2",
+model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc-c",
+ # spk_model="cam++",
)
res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
batch_size_s=300,
@@ -125,7 +124,7 @@
encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
-model = AutoModel(model="paraformer-zh-streaming", model_revision="v2.0.4")
+model = AutoModel(model="paraformer-zh-streaming")
import soundfile
import os
@@ -148,17 +147,19 @@
```python
from funasr import AutoModel
-model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
+model = AutoModel(model="fsmn-vad")
wav_file = f"{model.model_path}/example/asr_example.wav"
res = model.generate(input=wav_file)
print(res)
```
+Note: The output format of the VAD model is: `[[beg1, end1], [beg2, end2], ..., [begN, endN]]`, where `begN/endN` indicates the starting/ending point of the `N-th` valid audio segment, measured in milliseconds.
+
### Voice Activity Detection (Streaming)
```python
from funasr import AutoModel
chunk_size = 200 # ms
-model = AutoModel(model="fsmn-vad", model_revision="v2.0.4")
+model = AutoModel(model="fsmn-vad")
import soundfile
@@ -175,11 +176,18 @@
if len(res[0]["value"]):
print(res)
```
+Note: The output format for the streaming VAD model can be one of four scenarios:
+- `[[beg1, end1], [beg2, end2], .., [begN, endN]]`锛歍he same as the offline VAD output result mentioned above.
+- `[[beg, -1]]`锛欼ndicates that only a starting point has been detected.
+- `[[-1, end]]`锛欼ndicates that only an ending point has been detected.
+- `[]`锛欼ndicates that neither a starting point nor an ending point has been detected.
+
+The output is measured in milliseconds and represents the absolute time from the starting point.
### Punctuation Restoration
```python
from funasr import AutoModel
-model = AutoModel(model="ct-punc", model_revision="v2.0.4")
+model = AutoModel(model="ct-punc")
res = model.generate(input="閭ｄ粖澶╃殑浼氬氨鍒拌繖閲屽惂 happy new year 鏄庡勾瑙�")
print(res)
```
@@ -187,7 +195,7 @@
```python
from funasr import AutoModel
-model = AutoModel(model="fa-zh", model_revision="v2.0.4")
+model = AutoModel(model="fa-zh")
wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/text.txt"
res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
--
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