Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload config
Browse files- config.json +2 -1
config.json
CHANGED
|
@@ -1,4 +1,5 @@
|
|
| 1 |
{
|
|
|
|
| 2 |
"activation_dropout": 0.0,
|
| 3 |
"activation_function": "gelu",
|
| 4 |
"attention_dropout": 0.0,
|
|
@@ -36,7 +37,7 @@
|
|
| 36 |
"model_type": "whisper",
|
| 37 |
"num_hidden_layers": 32,
|
| 38 |
"num_mel_bins": 80,
|
| 39 |
-
"pad_token_id":
|
| 40 |
"scale_embedding": false,
|
| 41 |
"suppress_tokens": [
|
| 42 |
1,
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "openai/whisper-large",
|
| 3 |
"activation_dropout": 0.0,
|
| 4 |
"activation_function": "gelu",
|
| 5 |
"attention_dropout": 0.0,
|
|
|
|
| 37 |
"model_type": "whisper",
|
| 38 |
"num_hidden_layers": 32,
|
| 39 |
"num_mel_bins": 80,
|
| 40 |
+
"pad_token_id": 50257,
|
| 41 |
"scale_embedding": false,
|
| 42 |
"suppress_tokens": [
|
| 43 |
1,
|