timit-asr/timit_asr
Updated โข 271 โข 27
How to use jimregan/wav2vec2-xls-r-300m-phoneme-timit with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="jimregan/wav2vec2-xls-r-300m-phoneme-timit") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("jimregan/wav2vec2-xls-r-300m-phoneme-timit")
model = AutoModelForCTC.from_pretrained("jimregan/wav2vec2-xls-r-300m-phoneme-timit", device_map="auto")This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None 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 | Cer |
|---|---|---|---|---|---|
| 3.5325 | 11.9 | 1000 | 3.4897 | 1.0 | 0.9266 |
| 2.1973 | 23.81 | 2000 | 1.1350 | 0.8396 | 0.2403 |
| 1.4762 | 35.71 | 3000 | 0.5270 | 0.6845 | 0.1563 |
| 1.2409 | 47.62 | 4000 | 0.4195 | 0.6331 | 0.1403 |
| 1.1241 | 59.52 | 5000 | 0.3845 | 0.6362 | 0.1379 |
| 1.024 | 71.43 | 6000 | 0.3716 | 0.6321 | 0.1355 |
| 0.9922 | 83.33 | 7000 | 0.3728 | 0.6290 | 0.1331 |
| 0.9432 | 95.24 | 8000 | 0.3648 | 0.6170 | 0.1321 |
| 0.9279 | 107.14 | 9000 | 0.3643 | 0.6248 | 0.1325 |
| 0.9268 | 119.05 | 10000 | 0.3630 | 0.6243 | 0.1316 |
Base model
facebook/wav2vec2-xls-r-300m