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wav2vec2-large-mms-1b-chich-colab

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
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dmatekenya commited on
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End of training

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  1. README.md +47 -4
  2. adapter.nya.safetensors +1 -1
  3. pytorch_model.bin +1 -1
README.md CHANGED
@@ -18,7 +18,7 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: inf
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- - Wer: 1.0019
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  ## Model description
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@@ -44,15 +44,58 @@ The following hyperparameters were used during training:
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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: 100
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- - num_epochs: 1
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 7.1814 | 0.44 | 100 | inf | 0.9956 |
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- | 3.7822 | 0.88 | 200 | inf | 1.0019 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset.
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  It achieves the following results on the evaluation set:
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  - Loss: inf
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+ - Wer: 0.7342
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  ## Model description
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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: 100
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+ - num_epochs: 20
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 3.4078 | 0.44 | 100 | inf | 1.0010 |
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+ | 3.3151 | 0.88 | 200 | inf | 0.9997 |
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+ | 1.758 | 1.32 | 300 | inf | 0.7672 |
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+ | 1.3904 | 1.75 | 400 | inf | 0.7329 |
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+ | 1.2602 | 2.19 | 500 | inf | 0.7159 |
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+ | 1.1833 | 2.63 | 600 | inf | 0.7149 |
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+ | 1.263 | 3.07 | 700 | inf | 0.7166 |
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+ | 1.1387 | 3.51 | 800 | inf | 0.6955 |
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+ | 1.0807 | 3.95 | 900 | inf | 0.7006 |
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+ | 1.2017 | 4.39 | 1000 | inf | 0.6910 |
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+ | 1.0561 | 4.82 | 1100 | inf | 0.6858 |
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+ | 1.099 | 5.26 | 1200 | inf | 0.6961 |
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+ | 1.0189 | 5.7 | 1300 | inf | 0.6726 |
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+ | 1.026 | 6.14 | 1400 | inf | 0.7083 |
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+ | 1.0125 | 6.58 | 1500 | inf | 0.6934 |
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+ | 0.977 | 7.02 | 1600 | inf | 0.6781 |
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+ | 0.988 | 7.46 | 1700 | inf | 0.6783 |
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+ | 0.999 | 7.89 | 1800 | inf | 0.6880 |
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+ | 0.9678 | 8.33 | 1900 | inf | 0.6807 |
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+ | 0.9463 | 8.77 | 2000 | inf | 0.6744 |
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+ | 0.8928 | 9.21 | 2100 | inf | 0.6701 |
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+ | 0.9399 | 9.65 | 2200 | inf | 0.6813 |
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+ | 0.9535 | 10.09 | 2300 | inf | 0.6862 |
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+ | 0.8886 | 10.53 | 2400 | inf | 0.6694 |
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+ | 0.9367 | 10.96 | 2500 | inf | 0.6825 |
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+ | 0.9035 | 11.4 | 2600 | inf | 0.6724 |
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+ | 0.8658 | 11.84 | 2700 | inf | 0.6554 |
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+ | 0.9042 | 12.28 | 2800 | inf | 0.6644 |
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+ | 0.8668 | 12.72 | 2900 | inf | 0.6618 |
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+ | 0.8843 | 13.16 | 3000 | inf | 0.6689 |
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+ | 0.8504 | 13.6 | 3100 | inf | 0.6589 |
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+ | 0.8462 | 14.04 | 3200 | inf | 0.6619 |
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+ | 0.8472 | 14.47 | 3300 | inf | 0.6729 |
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+ | 0.8375 | 14.91 | 3400 | inf | 0.6575 |
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+ | 0.7992 | 15.35 | 3500 | inf | 0.6850 |
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+ | 0.8593 | 15.79 | 3600 | inf | 0.6905 |
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+ | 0.8069 | 16.23 | 3700 | inf | 0.7018 |
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+ | 0.8884 | 16.67 | 3800 | inf | 0.6963 |
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+ | 0.8578 | 17.11 | 3900 | inf | 0.7070 |
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+ | 0.8411 | 17.54 | 4000 | inf | 0.7214 |
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+ | 0.8276 | 17.98 | 4100 | inf | 0.6968 |
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+ | 0.8659 | 18.42 | 4200 | inf | 0.6958 |
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+ | 0.9367 | 18.86 | 4300 | inf | 0.7568 |
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+ | 0.9912 | 19.3 | 4400 | inf | 0.7080 |
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+ | 0.9682 | 19.74 | 4500 | inf | 0.7342 |
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  ### Framework versions
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