whisper-fa-tinyyy

This model is a fine-tuned version of openai/whisper-tiny on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0246

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: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.0186	0.9998	2357	0.0246

Framework versions

• Transformers 4.49.0
• Pytorch 2.6.0+cu124
• Datasets 3.4.1
• Tokenizers 0.21.1

how to use the model in colab:

# Install required packages
!pip install torch torchaudio transformers pydub google-colab

import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from pydub import AudioSegment
import os
from google.colab import files

# Load the model and processor
model_id = "hackergeek98/whisper-fa-tinyyy"
device = "cuda" if torch.cuda.is_available() else "cpu"

model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id).to(device)
processor = AutoProcessor.from_pretrained(model_id)

# Create pipeline
whisper_pipe = pipeline(
    "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, device=0 if torch.cuda.is_available() else -1
)

# Convert audio to WAV format
def convert_to_wav(audio_path):
    audio = AudioSegment.from_file(audio_path)
    wav_path = "converted_audio.wav"
    audio.export(wav_path, format="wav")
    return wav_path

# Split long audio into chunks
def split_audio(audio_path, chunk_length_ms=30000):  # Default: 30 sec per chunk
    audio = AudioSegment.from_wav(audio_path)
    chunks = [audio[i:i+chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)]
    chunk_paths = []
    
    for i, chunk in enumerate(chunks):
        chunk_path = f"chunk_{i}.wav"
        chunk.export(chunk_path, format="wav")
        chunk_paths.append(chunk_path)
    
    return chunk_paths

# Transcribe a long audio file
def transcribe_long_audio(audio_path):
    wav_path = convert_to_wav(audio_path)
    chunk_paths = split_audio(wav_path)
    transcription = ""
    
    for chunk in chunk_paths:
        result = whisper_pipe(chunk)
        transcription += result["text"] + "\n"
        os.remove(chunk)  # Remove processed chunk
    
    os.remove(wav_path)  # Cleanup original file
    
    # Save transcription to a text file
    text_path = "transcription.txt"
    with open(text_path, "w") as f:
        f.write(transcription)
    
    return text_path

# Upload and process audio in Colab
uploaded = files.upload()
audio_file = list(uploaded.keys())[0]
transcription_file = transcribe_long_audio(audio_file)

# Download the transcription file
files.download(transcription_file)