End of training

README.md

@@ -1,65 +1,83 @@
 ---
-license: mit
-datasets:
-- mozilla-foundation/common_voice_17_0
+library_name: transformers
 language:
-- en
 - ta
+license: apache-2.0
+base_model: openai/whisper-small
+tags:
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_11_0
 metrics:
 - wer
-base_model:
-- openai/whisper-small
-pipeline_tag: automatic-speech-recognition
-library_name: transformers
-tags:
-- language-identification
-- speech-to-text 
+model-index:
+- name: Whisper Small ta - Lingalingeswaran
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 11.0
+      type: mozilla-foundation/common_voice_11_0
+      config: ta
+      split: None
+      args: 'config: ta, split: test'
+    metrics:
+    - name: Wer
+      type: wer
+      value: 43.31959037105998
+---
 
+<!-- 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. -->
 
+# Whisper Small ta - Lingalingeswaran
 
----
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2150
+- Wer: 43.3196
+
+## Model description
+
+More information needed
 
-# Whisper-small-ta
-This model is trainned for voice to text trancription for tamil language
+## Intended uses & limitations
 
-## Model Overview
-This model is fine-tuned from `openai/whisper-small` using the [Mozilla Common Voice 17.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_17_0) dataset for language identification and transcription in **Tamil** . The model is designed to accurately transcribe spoken audio into text and identify whether the language is Tamil .
+More information needed
 
-### Key Features:
-- **Languages**: Tamil
-- **Base Model**: Whisper-small from OpenAI
-- **Dataset**: Mozilla Common Voice 17.0
+## Training and evaluation data
 
-## Intended Use
-The model is designed for automatic speech recognition (ASR) in Tamil, making it suitable for transcription and language identification in real-time applications.
+More information needed
 
-## Training Details
-This model was fine-tuned using a subset of the Mozilla Common Voice dataset. The dataset contains '53,468 ' samples  
+## Training procedure
 
-### Fine-tuning Process:
-- The fine-tuning was performed on `Whisper-small`, a smaller version of OpenAI's Whisper model, for reduced latency and higher accuracy for low-resource languages.
-- The model was trained for `2` epochs on a `Google Colab Pro` environment.
+### Training hyperparameters
 
-## Performance
-The model achieved a **Word Error Rate (WER)** of `34%` , using a validation dataset with `8` hours of audio.
-We expect further improvements with continued training.
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 4000
+- mixed_precision_training: Native AMP
 
-## Usage
-You can use this model with the following code:
+### Training results
 
-```python
-from transformers import WhisperForConditionalGeneration, WhisperProcessor
-import torch
+| Training Loss | Epoch  | Step | Validation Loss | Wer     |
+|:-------------:|:------:|:----:|:---------------:|:-------:|
+| 0.1753        | 0.2992 | 1000 | 0.2705          | 51.0174 |
+| 0.1404        | 0.5984 | 2000 | 0.2368          | 46.9969 |
+| 0.1344        | 0.8977 | 3000 | 0.2196          | 44.5325 |
+| 0.0947        | 1.1969 | 4000 | 0.2150          | 43.3196 |
 
-model = WhisperForConditionalGeneration.from_pretrained("Lingalingeswaran/whisper-small-ta")
-processor = WhisperProcessor.from_pretrained("Lingalingeswaran/whisper-small-ta")
 
-# Example audio input
-audio = "path_to_audio_file"
+### Framework versions
 
-inputs = processor(audio, return_tensors="pt", padding="longest")
-with torch.no_grad():
-    predicted_ids = model.generate(inputs.input_ids)
-    
-transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
-print(transcription)
+- Transformers 4.45.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.20.1

generation_config.json

@@ -0,0 +1,254 @@
+{
+  "alignment_heads": [
+    [
+      5,
+      3
+    ],
+    [
+      5,
+      9
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+    [
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+      0
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+    [
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+      4
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+    [
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+      7
+    ],
+    [
+      8,
+      8
+    ],
+    [
+      9,
+      0
+    ],
+    [
+      9,
+      7
+    ],
+    [
+      9,
+      9
+    ],
+    [
+      10,
+      5
+    ]
+  ],
+  "begin_suppress_tokens": [
+    220,
+    50257
+  ],
+  "bos_token_id": 50257,
+  "decoder_start_token_id": 50258,
+  "eos_token_id": 50257,
+  "is_multilingual": true,
+  "lang_to_id": {
+    "<|af|>": 50327,
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+    "<|vi|>": 50278,
+    "<|yi|>": 50335,
+    "<|yo|>": 50325,
+    "<|zh|>": 50260
+  },
+  "language": "tamil",
+  "max_initial_timestamp_index": 50,
+  "max_length": 448,
+  "no_timestamps_token_id": 50363,
+  "pad_token_id": 50257,
+  "prev_sot_token_id": 50361,
+  "return_timestamps": false,
+  "suppress_tokens": [
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+  "task": "transcribe",
+  "task_to_id": {
+    "transcribe": 50359,
+    "translate": 50358
+  },
+  "transformers_version": "4.45.2"
+}