Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
Safetensors
English
hubert
speech
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use facebook/hubert-xlarge-ls960-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/hubert-xlarge-ls960-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/hubert-xlarge-ls960-ft")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/hubert-xlarge-ls960-ft") model = AutoModelForCTC.from_pretrained("facebook/hubert-xlarge-ls960-ft") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -59,7 +59,7 @@ model = HubertForCTC.from_pretrained("facebook/hubert-xlarge-ls960-ft")
|
|
| 59 |
|
| 60 |
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
|
| 61 |
|
| 62 |
-
input_values = processor(ds[0]["audio"]["array"],
|
| 63 |
logits = model(input_values).logits
|
| 64 |
predicted_ids = torch.argmax(logits, dim=-1)
|
| 65 |
transcription = processor.decode(predicted_ids[0])
|
|
|
|
| 59 |
|
| 60 |
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
|
| 61 |
|
| 62 |
+
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values # Batch size 1
|
| 63 |
logits = model(input_values).logits
|
| 64 |
predicted_ids = torch.argmax(logits, dim=-1)
|
| 65 |
transcription = processor.decode(predicted_ids[0])
|