Instructions to use onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration") model = AutoModelForSpeechSeq2Seq.from_pretrained("onnx-internal-testing/tiny-random-GraniteSpeechForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
Update preprocessor_config.json
Browse files- preprocessor_config.json +1 -1
preprocessor_config.json
CHANGED
|
@@ -3,7 +3,7 @@
|
|
| 3 |
"melspec_kwargs": {
|
| 4 |
"hop_length": 160,
|
| 5 |
"n_fft": 512,
|
| 6 |
-
"n_mels":
|
| 7 |
"sample_rate": 16000,
|
| 8 |
"win_length": 400
|
| 9 |
},
|
|
|
|
| 3 |
"melspec_kwargs": {
|
| 4 |
"hop_length": 160,
|
| 5 |
"n_fft": 512,
|
| 6 |
+
"n_mels": 8,
|
| 7 |
"sample_rate": 16000,
|
| 8 |
"win_length": 400
|
| 9 |
},
|