Setting	Value
Base model	`openai/whisper-small`
Dataset	`kethankrk/IndicVoices-sample-1000`
Language / task	Malayalam / transcribe
Learning rate	1e-5
Batch size	8
Final validation WER	~67.4%

Audio filtered to 1–30s; target transcripts capped at Whisper’s 448-token limit.

Usage

python

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="abhinav-spidey/Whisper-ml-v1",
    chunk_length_s=30,
)
print(pipe("path/to/audio.wav")["text"])

Or load the model and processor directly:

python

from transformers import WhisperForConditionalGeneration, WhisperProcessor
import torch

model_id = "abhinav-spidey/Whisper-ml-v1"
processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)

# inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")
# predicted_ids = model.generate(inputs.input_features)
# transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]

Intended use

Malayalam ASR for short clips (up to ~30s). Performance depends on accent, noise, and domain match to IndicVoices.