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W2V2-BERT-withLM-Malayalam

Automatic Speech Recognition
TensorBoard
Safetensors
Malayalam
wav2vec2-bert
Eval Results
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Duplicate from vrclc/W2V2-BERT-withLM-Malayalam

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Co-authored-by: Kavya Manohar <[email protected]>

Files changed (32) hide show
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README.md ADDED
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+ ---
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+ base_model: facebook/w2v-bert-2.0
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+ license: mit
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: W2V2-BERT-withLM-Malayalam by Bajiyo Baiju, Kavya Manohar
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+ results:
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+ - task:
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+ type: automatic-speech-recognition
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+ name: Automatic Speech Recognition
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+ dataset:
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+ name: OpenSLR Malayalam -Test
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+ type: vrclc/openslr63
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+ config: ml
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+ split: test
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+ args: ml
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+ metrics:
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+ - type: wer
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+ value: 18.23
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+ name: WER
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+ - task:
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+ type: automatic-speech-recognition
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+ name: Automatic Speech Recognition
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+ dataset:
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+ name: Google Fleurs
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+ type: google/fleurs
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+ config: ml
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+ split: test
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+ args: ml
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+ metrics:
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+ - type: wer
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+ value: 31.92
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+ name: WER
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+ - task:
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+ type: automatic-speech-recognition
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+ name: Automatic Speech Recognition
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+ dataset:
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+ name: Mozilla Common Voice
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+ type: mozilla-foundation/common_voice_16_1
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+ config: ml
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+ split: test
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+ args: ml
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+ metrics:
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+ - type: wer
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+ value: 49.79
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+ name: WER
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+ datasets:
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+ - vrclc/festvox-iiith-ml
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+ - vrclc/openslr63
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+ - vrclc/imasc_slr
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+ - mozilla-foundation/common_voice_17_0
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+ - smcproject/MSC
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+ - kavyamanohar/ml-sentences
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+ language:
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+ - ml
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+ pipeline_tag: automatic-speech-recognition
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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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+ # W2V2-BERT-withLM-Malayalam
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+
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+ This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the [IMASC](https://huggingface.co/datasets/thennal/IMaSC), [MSC](https://huggingface.co/datasets/smcproject/MSC), [OpenSLR Malayalam Train split](https://huggingface.co/datasets/vrclc/openslr63), [Festvox Malayalam](https://huggingface.co/datasets/vrclc/openslr63), [CV16](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_0) .
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+
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+ It achieves the following results on the validation set : [OpenSLR-Test](https://huggingface.co/vrclc/openslr63):
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+ - Loss: 0.1722
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+ - Wer: 0.1299
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+
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+ Trigram Language Model Trained using KENLM Library on [kavyamanohar/ml-sentences](https://huggingface.co/datasets/kavyamanohar/ml-sentences) dataset
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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: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 32
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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: 500
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+ - num_epochs: 10
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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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+ | 1.1416 | 0.46 | 600 | 0.3393 | 0.4616 |
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+ | 0.1734 | 0.92 | 1200 | 0.2414 | 0.3493 |
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+ | 0.1254 | 1.38 | 1800 | 0.2205 | 0.2963 |
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+ | 0.1097 | 1.84 | 2400 | 0.2157 | 0.3133 |
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+ | 0.0923 | 2.3 | 3000 | 0.1854 | 0.2473 |
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+ | 0.0792 | 2.76 | 3600 | 0.1939 | 0.2471 |
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+ | 0.0696 | 3.22 | 4200 | 0.1720 | 0.2282 |
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+ | 0.0589 | 3.68 | 4800 | 0.1768 | 0.2013 |
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+ | 0.0552 | 4.14 | 5400 | 0.1635 | 0.1864 |
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+ | 0.0437 | 4.6 | 6000 | 0.1501 | 0.1826 |
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+ | 0.0408 | 5.06 | 6600 | 0.1500 | 0.1645 |
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+ | 0.0314 | 5.52 | 7200 | 0.1559 | 0.1655 |
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+ | 0.0317 | 5.98 | 7800 | 0.1448 | 0.1553 |
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+ | 0.022 | 6.44 | 8400 | 0.1592 | 0.1590 |
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+ | 0.0218 | 6.9 | 9000 | 0.1431 | 0.1458 |
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+ | 0.0154 | 7.36 | 9600 | 0.1514 | 0.1366 |
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+ | 0.0141 | 7.82 | 10200 | 0.1540 | 0.1383 |
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+ | 0.0113 | 8.28 | 10800 | 0.1558 | 0.1391 |
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+ | 0.0085 | 8.74 | 11400 | 0.1612 | 0.1356 |
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+ | 0.0072 | 9.2 | 12000 | 0.1697 | 0.1289 |
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+ | 0.0046 | 9.66 | 12600 | 0.1722 | 0.1299 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.39.3
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.1
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+ {"labels": [" ", "\u0d02", "\u0d03", "\u0d05", "\u0d06", "\u0d07", "\u0d08", "\u0d09", "\u0d0a", "\u0d0b", "\u0d0e", "\u0d0f", "\u0d10", "\u0d12", "\u0d13", "\u0d14", "\u0d15", "\u0d16", "\u0d17", "\u0d18", "\u0d19", "\u0d1a", "\u0d1b", "\u0d1c", "\u0d1d", "\u0d1e", "\u0d1f", "\u0d20", "\u0d21", "\u0d22", "\u0d23", "\u0d24", "\u0d25", "\u0d26", "\u0d27", "\u0d28", "\u0d2a", "\u0d2b", "\u0d2c", "\u0d2d", "\u0d2e", "\u0d2f", "\u0d30", "\u0d31", "\u0d32", "\u0d33", "\u0d34", "\u0d35", "\u0d36", "\u0d37", "\u0d38", "\u0d39", "\u0d3e", "\u0d3f", "\u0d40", "\u0d41", "\u0d42", "\u0d43", "\u0d46", "\u0d47", "\u0d48", "\u0d4a", "\u0d4b", "\u0d4c", "\u0d4d", "\u0d57", "\u0d7a", "\u0d7b", "\u0d7c", "\u0d7d", "\u0d7e", "\u0d7f", "\u200c", "\u200d", "\u2019", "\u2047", "", "<s>", "</s>"], "is_bpe": false}
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