jayasuryajsk/whisper-large-v3-Telugu-Romanized

<!-- 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. -->

Wishper Large V3 - Romanized Spoken Telugu

This model is a fine-tuned version of openai/whisper-large-v3 on the Telugu Romanized 1.0 dataset. It achieves the following results on the evaluation set:

• —eval_loss: 1.5009
• —eval_wer: 68.1275
• —eval_runtime: 591.6137
• —evalsamplesper_second: 0.798
• —evalstepsper_second: 0.1
• —epoch: 8.6207
• —step: 1000

Model description

The model is trained to transcipt Telugu conversations in Romanized script, that most people uses in day to day life.

Intended uses & limitations

Limitations: Sometimes, it translates the audio to english directly. Working on this to fix it.

Training and evaluation data

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.

Usage

from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "jayasuryajsk/whisper-large-v3-Telugu-Romanized"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    chunk_length_s=30,
    batch_size=16,
    return_timestamps=True,
    torch_dtype=torch_dtype,
    device=device,
)
result = pipe("recording.mp3", generate_kwargs={"language": "english"})
print(result["text"])

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 1e-05
• —trainbatchsize: 20
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 500
• —training_steps: 2000
• —mixedprecisiontraining: Native AMP

Framework versions

• —Transformers 4.40.1
• —Pytorch 2.2.0+cu121
• —Datasets 2.19.1
• —Tokenizers 0.19.1