legacy-datasets/ami
Updated • 209 • 25
How to use tz579/wav2vec2-large-ami-fine-tuned with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("automatic-speech-recognition", model="tz579/wav2vec2-large-ami-fine-tuned") # Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("tz579/wav2vec2-large-ami-fine-tuned")
model = AutoModelForCTC.from_pretrained("tz579/wav2vec2-large-ami-fine-tuned")This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the EDINBURGHCSTR/AMI - IHM 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 |
|---|---|---|---|---|
| 1.5455 | 0.1565 | 1000 | 1.3698 | 0.8373 |
| 1.3019 | 0.3131 | 2000 | 0.7275 | 0.4146 |
| 0.9922 | 0.4696 | 3000 | 0.6047 | 0.3663 |
| 0.5129 | 0.6262 | 4000 | 0.5773 | 0.3658 |
| 0.85 | 0.7827 | 5000 | 0.5387 | 0.3538 |
| 1.4588 | 0.9393 | 6000 | 0.5581 | 0.3326 |
| 0.2646 | 1.0958 | 7000 | 0.5216 | 0.3294 |
| 0.1923 | 1.2523 | 8000 | 0.4975 | 0.3159 |
| 0.2897 | 1.4089 | 9000 | 0.4757 | 0.3066 |
| 0.1536 | 1.5654 | 10000 | 0.4784 | 0.3066 |
| 0.3964 | 1.7220 | 11000 | 0.4899 | 0.3097 |
| 1.1026 | 1.8785 | 12000 | 0.9830 | 0.8711 |
Base model
facebook/wav2vec2-large-lv60