speech_recognition/FunASR
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funasr1.0

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游雁 2024-01-15 14:20:24 +08:00
parent c6361cc2a7
commit a035d68e86
2 changed files with 6 additions and 28 deletions
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17
README.md
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@ -122,13 +122,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
res = model(input=speech_chunk,
cache=cache,
is_final=is_final,
chunk_size=chunk_size,
encoder_chunk_look_back=encoder_chunk_look_back,
decoder_chunk_look_back=decoder_chunk_look_back,
)
res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
print(res)
```
Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word.
@ -161,11 +155,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
res = model(input=speech_chunk,
cache=cache,
is_final=is_final,
chunk_size=chunk_size,
)
res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
if len(res[0]["value"]):
print(res)
```
@ -186,8 +176,7 @@ model = AutoModel(model="fa-zh", model_revision="v2.0.0")
wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/asr_example.wav"
res = model(input=(wav_file, text_file),
data_type=("sound", "text"))
res = model(input=(wav_file, text_file), data_type=("sound", "text"))
print(res)
```
[//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):)

17
README_zh.md
View File

@ -119,13 +119,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
res = model(input=speech_chunk,
cache=cache,
is_final=is_final,
chunk_size=chunk_size,
encoder_chunk_look_back=encoder_chunk_look_back,
decoder_chunk_look_back=decoder_chunk_look_back,
)
res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
print(res)
```
@ -160,11 +154,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
res = model(input=speech_chunk,
cache=cache,
is_final=is_final,
chunk_size=chunk_size,
)
res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
if len(res[0]["value"]):
print(res)
```
@ -187,8 +177,7 @@ model = AutoModel(model="fa-zh", model_revision="v2.0.0")
wav_file = f"{model.model_path}/example/asr_example.wav"
text_file = f"{model.model_path}/example/asr_example.wav"
res = model(input=(wav_file, text_file),
data_type=("sound", "text"))
res = model(input=(wav_file, text_file), data_type=("sound", "text"))
print(res)
```
更多详细用法（[示例](examples/industrial_data_pretraining)）