vad

游雁

2024-02-27 a3bb4013c39faa1d006dcb4d6d87ec9a6bb3770c

 README.md

@@ -105,10 +105,8 @@
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 +123,7 @@
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
@@ -148,7 +146,7 @@
```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)
@@ -160,7 +158,7 @@
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

@@ -188,7 +186,7 @@
```python
from funasr import AutoModel

model = AutoModel(model="ct-punc", model_revision="v2.0.4")
model = AutoModel(model="ct-punc")
res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
print(res)
```
@@ -196,7 +194,7 @@
```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"))