fixie-ai/common_voice_17_0
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How to use mmarron14/whisper-medium-cv17-es-5-steps_sin_proc-def4 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="mmarron14/whisper-medium-cv17-es-5-steps_sin_proc-def4") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("mmarron14/whisper-medium-cv17-es-5-steps_sin_proc-def4")
model = AutoModelForSpeechSeq2Seq.from_pretrained("mmarron14/whisper-medium-cv17-es-5-steps_sin_proc-def4", device_map="auto")This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| No log | 0.2 | 1 | 0.6327 | 12.5828 | 7.1257 |
| No log | 0.4 | 2 | 0.5409 | 12.3581 | 7.0316 |
| No log | 0.6 | 3 | 0.4270 | 12.3989 | 7.0393 |
| No log | 0.8 | 4 | 0.3494 | 12.4352 | 7.0431 |
| No log | 1.0 | 5 | 0.3184 | 12.5020 | 7.0621 |
Base model
openai/whisper-medium