facebook/multilingual_librispeech
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How to use sgangireddy/whisper-largev2-mls-it with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="sgangireddy/whisper-largev2-mls-it") # Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("sgangireddy/whisper-largev2-mls-it")
model = AutoModelForSpeechSeq2Seq.from_pretrained("sgangireddy/whisper-largev2-mls-it")This model is a fine-tuned version of openai/whisper-large-v2 on the facebook/multilingual_librispeech italian dataset. It achieves the following results on the evaluation set:
The model is fine-tuned for 4000 updates/steps on multilingual librispeech Italian train data.
More information needed
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1115 | 1.02 | 1000 | 0.2116 | 9.4217 |
| 0.0867 | 2.03 | 2000 | 0.1964 | 9.7823 |
| 0.0447 | 3.05 | 3000 | 0.2001 | 9.6409 |
| 0.0426 | 4.07 | 4000 | 0.2051 | 8.3353 |