ndeclarke
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wav2vec2-mms-1b-malayalam-colab-CV17.0-v2

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Safetensors

wav2vec2

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ndeclarke commited on Sep 3

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: cc-by-nc-4.0
+base_model: facebook/mms-1b-all
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_17_0
+metrics:
+- wer
+- bleu
+model-index:
+- name: wav2vec2-mms-1b-malayalam-colab-CV17.0-v2
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_17_0
+      type: common_voice_17_0
+      config: ml
+      split: test
+      args: ml
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.5283687943262412
+    - name: Bleu
+      type: bleu
+      value: 0.1996948603256558
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# wav2vec2-mms-1b-malayalam-colab-CV17.0-v2
+This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the common_voice_17_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2965
+- Wer: 0.5284
+- Cer: 0.0934
+- Bleu: 0.1997
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.001
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.15
+- training_steps: 2000
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    | Bleu   |
+|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|:------:|
+| 5.5563        | 3.1496  | 200  | 0.3157          | 0.5580 | 0.1055 | 0.1800 |
+| 0.3888        | 6.2992  | 400  | 0.2983          | 0.5471 | 0.1003 | 0.1906 |
+| 0.3328        | 9.4488  | 600  | 0.3008          | 0.5542 | 0.1002 | 0.1634 |
+| 0.3006        | 12.5984 | 800  | 0.2821          | 0.5368 | 0.0984 | 0.1888 |
+| 0.2743        | 15.7480 | 1000 | 0.2913          | 0.5329 | 0.0968 | 0.1813 |
+| 0.2461        | 18.8976 | 1200 | 0.2822          | 0.5319 | 0.0957 | 0.1937 |
+| 0.2346        | 22.0472 | 1400 | 0.2933          | 0.5335 | 0.0942 | 0.1848 |
+| 0.2112        | 25.1969 | 1600 | 0.2885          | 0.5300 | 0.0947 | 0.1900 |
+| 0.2006        | 28.3465 | 1800 | 0.2944          | 0.5329 | 0.0939 | 0.1870 |
+| 0.1879        | 31.4961 | 2000 | 0.2965          | 0.5284 | 0.0934 | 0.1997 |
+### Framework versions
+- Transformers 4.42.4
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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