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patrickvonplaten
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unispeech-sat-large-timit-ft

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
unispeech-sat
timit_asr
generated_from_trainer
Inference Endpoints
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+ ---
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+ tags:
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+ - automatic-speech-recognition
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+ - timit_asr
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+ - generated_from_trainer
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+ datasets:
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+ - timit_asr
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+ model-index:
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+ - name: unispeech-sat-large-timit-ft
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # unispeech-sat-large-timit-ft
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+
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+ This model is a fine-tuned version of [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) on the TIMIT_ASR - NA dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6074
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+ - Wer: 0.3880
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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+ - train_batch_size: 32
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 1000
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+ - num_epochs: 20.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 6.2516 | 0.69 | 100 | 5.8638 | 1.0 |
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+ | 2.9596 | 1.38 | 200 | 2.9550 | 1.0 |
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+ | 2.8831 | 2.07 | 300 | 2.8547 | 1.0 |
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+ | 2.3223 | 2.76 | 400 | 2.2044 | 1.0063 |
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+ | 1.2104 | 3.45 | 500 | 1.0845 | 0.7706 |
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+ | 0.6779 | 4.14 | 600 | 0.7342 | 0.5663 |
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+ | 0.6319 | 4.83 | 700 | 0.6054 | 0.4881 |
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+ | 0.664 | 5.52 | 800 | 0.5808 | 0.4913 |
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+ | 0.402 | 6.21 | 900 | 0.5647 | 0.4611 |
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+ | 0.3176 | 6.9 | 1000 | 0.5211 | 0.4440 |
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+ | 0.3392 | 7.59 | 1100 | 0.5187 | 0.4359 |
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+ | 0.3888 | 8.28 | 1200 | 0.5501 | 0.4391 |
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+ | 0.2874 | 8.97 | 1300 | 0.5249 | 0.4148 |
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+ | 0.208 | 9.66 | 1400 | 0.5407 | 0.4152 |
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+ | 0.1457 | 10.34 | 1500 | 0.5722 | 0.4155 |
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+ | 0.2375 | 11.03 | 1600 | 0.5780 | 0.4059 |
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+ | 0.2111 | 11.72 | 1700 | 0.5823 | 0.4094 |
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+ | 0.1422 | 12.41 | 1800 | 0.5754 | 0.3977 |
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+ | 0.125 | 13.1 | 1900 | 0.5784 | 0.4031 |
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+ | 0.1996 | 13.79 | 2000 | 0.5630 | 0.3956 |
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+ | 0.1747 | 14.48 | 2100 | 0.5880 | 0.3964 |
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+ | 0.1263 | 15.17 | 2200 | 0.5987 | 0.3951 |
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+ | 0.11 | 15.86 | 2300 | 0.5688 | 0.3964 |
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+ | 0.1411 | 16.55 | 2400 | 0.6223 | 0.3906 |
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+ | 0.1647 | 17.24 | 2500 | 0.6135 | 0.3960 |
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+ | 0.1162 | 17.93 | 2600 | 0.6224 | 0.3960 |
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+ | 0.098 | 18.62 | 2700 | 0.6017 | 0.3907 |
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+ | 0.1183 | 19.31 | 2800 | 0.6121 | 0.3885 |
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+ | 0.1717 | 20.0 | 2900 | 0.6074 | 0.3880 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.12.0.dev0
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+ - Pytorch 1.8.1
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+ - Datasets 1.14.1.dev0
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+ - Tokenizers 0.10.3