speech_recognition/SenseVoice
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@ -118,7 +118,7 @@ from funasr.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
model = AutoModel (
model = AutoModel(
model=model_dir,
trust_remote_code=True,
remote_code="./model.py",
@ -128,17 +128,17 @@ model = AutoModel (
)
# en
res = model.generate (
res = model.generate(
input=f"{model.model_path}/example/en.mp3",
cache={},
language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
use_itn=True,
batch_size_s=60,
merge_vad=True, #
merge_vad=True,
merge_length_s=15,
)
text = rich_transcription_postprocess (res [0]["text"])
print (text)
text = rich_transcription_postprocess(res[0]["text"])
print(text)
```
<details><summary> 参数说明（点击展开）</summary>
@ -159,9 +159,9 @@ print (text)
如果输入均为短音频（小于 30s），并且需要批量化推理，为了加快推理效率，可以移除 vad 模型，并设置 `batch_size`
```python
model = AutoModel (model=model_dir, trust_remote_code=True, device="cuda:0")
model = AutoModel(model=model_dir, trust_remote_code=True, device="cuda:0")
res = model.generate (
res = model.generate(
input=f"{model.model_path}/example/en.mp3",
cache={},
language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
@ -181,10 +181,10 @@ from model import SenseVoiceSmall
from funasr.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
m, kwargs = SenseVoiceSmall.from_pretrained (model=model_dir, device="cuda:0")
m.eval ()
m, kwargs = SenseVoiceSmall.from_pretrained(model=model_dir, device="cuda:0")
m.eval()
res = m.inference (
res = m.inference(
data_in=f"{kwargs ['model_path']}/example/en.mp3",
language="auto", # "zh", "en", "yue", "ja", "ko", "nospeech"
use_itn=False,
@ -192,8 +192,8 @@ res = m.inference (
**kwargs,
)
text = rich_transcription_postprocess (res [0][0]["text"])
print (text)
text = rich_transcription_postprocess(res [0][0]["text"])
print(text)
```
## 服务部署
@ -215,13 +215,13 @@ from funasr_onnx.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
model = SenseVoiceSmall (model_dir, batch_size=10, quantize=True)
model = SenseVoiceSmall(model_dir, batch_size=10, quantize=True)
# inference
wav_or_scp = ["{}/.cache/modelscope/hub/{}/example/en.mp3".format (Path.home (), model_dir)]
wav_or_scp = ["{}/.cache/modelscope/hub/{}/example/en.mp3".format(Path.home(), model_dir)]
res = model (wav_or_scp, language="auto", use_itn=True)
print ([rich_transcription_postprocess (i) for i in res])
res = model(wav_or_scp, language="auto", use_itn=True)
print([rich_transcription_postprocess(i) for i in res])
```
备注：ONNX 模型导出到原模型目录中
@ -236,12 +236,12 @@ from funasr_torch.utils.postprocess_utils import rich_transcription_postprocess
model_dir = "iic/SenseVoiceSmall"
model = SenseVoiceSmall (model_dir, batch_size=10, device="cuda:0")
model = SenseVoiceSmall(model_dir, batch_size=10, device="cuda:0")
wav_or_scp = ["{}/.cache/modelscope/hub/{}/example/en.mp3".format (Path.home (), model_dir)]
wav_or_scp = ["{}/.cache/modelscope/hub/{}/example/en.mp3".format(Path.home(), model_dir)]
res = model (wav_or_scp, language="auto", use_itn=True)
print ([rich_transcription_postprocess (i) for i in res])
res = model(wav_or_scp, language="auto", use_itn=True)
print([rich_transcription_postprocess (i) for i in res])
```
备注：Libtorch 模型导出到原模型目录中