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--- |
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language: |
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- tel |
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license: apache-2.0 |
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base_model: openai/whisper-large-v3 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- jayasuryajsk/google-fleurs-te-romanized |
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model-index: |
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- name: Wishper-Large-V3-spoken_telugu_romanized |
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results: [] |
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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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# Wishper Large V3 - Romanized Spoken Telugu |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Telugu Romanized 1.0 dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 1.5009 |
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- eval_wer: 68.1275 |
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- eval_runtime: 591.6137 |
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- eval_samples_per_second: 0.798 |
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- eval_steps_per_second: 0.1 |
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- epoch: 8.6207 |
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- step: 1000 |
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## Model description |
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The model is trained to transcipt Telugu conversations in Romanized script, that most people uses in day to day life. |
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## Intended uses & limitations |
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Limitations: Sometimes, it translates the audio to english directly. Working on this to fix it. |
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## Training and evaluation data |
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Gpt 4 api was used to convert ``` google-fleurs ``` telugu labels to romanized script. I used english tokenizer, since the script is in english alphabet to train the model. |
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## Usage |
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```python |
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline |
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import torch |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 |
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model_id = "jayasuryajsk/whisper-large-v3-Telugu-Romanized" |
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model = AutoModelForSpeechSeq2Seq.from_pretrained( |
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model_id, torch_dtype=torch_dtype |
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) |
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model.to(device) |
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processor = AutoProcessor.from_pretrained(model_id) |
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pipe = pipeline( |
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"automatic-speech-recognition", |
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model=model, |
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tokenizer=processor.tokenizer, |
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feature_extractor=processor.feature_extractor, |
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max_new_tokens=128, |
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chunk_length_s=30, |
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batch_size=16, |
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return_timestamps=True, |
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torch_dtype=torch_dtype, |
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device=device, |
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) |
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result = pipe("recording.mp3", generate_kwargs={"language": "english"}) |
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print(result["text"]) |
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``` |
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Try this on https://colab.research.google.com/drive/1KxWSaxZThv8PE4mDoLfJv0O7L-5hQ1lE?usp=sharing |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 20 |
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- eval_batch_size: 8 |
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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: 500 |
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- training_steps: 2000 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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