Automatic Speech Recognition
NeMo
PyTorch
English
speech
audio
CTC
Citrinet
Transformer
NeMo
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use nvidia/stt_en_citrinet_256_ls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use nvidia/stt_en_citrinet_256_ls with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("nvidia/stt_en_citrinet_256_ls") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -98,7 +98,8 @@ wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
|
|
| 98 |
```
|
| 99 |
Then simply do:
|
| 100 |
```
|
| 101 |
-
asr_model.transcribe(['2086-149220-0033.wav'])
|
|
|
|
| 102 |
```
|
| 103 |
|
| 104 |
### Transcribing many audio files
|
|
|
|
| 98 |
```
|
| 99 |
Then simply do:
|
| 100 |
```
|
| 101 |
+
output = asr_model.transcribe(['2086-149220-0033.wav'])
|
| 102 |
+
print(output[0].text)
|
| 103 |
```
|
| 104 |
|
| 105 |
### Transcribing many audio files
|