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---
license: mit
datasets:
- mozilla-foundation/common_voice_11_0
language:
- fa
metrics:
- wer
base_model:
- openai/whisper-tiny
pipeline_tag: automatic-speech-recognition
library_name: transformers
---
this model trained on validation segment of data for one epoch with 0.05 loss and tested on test segment of data with 0.07 loss and WER: 1.636687802644541
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/tinyyyy_whisper"
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
# Transcribe an audio file and save as text
def transcribe(audio_path):
wav_path = convert_to_wav(audio_path)
result = whisper_pipe(wav_path)
os.remove(wav_path) # Cleanup temporary file
# Save transcription to a text file
text_path = "transcription.txt"
with open(text_path, "w") as f:
f.write(result["text"])
return text_path
# Upload and process audio in Colab
uploaded = files.upload()
audio_file = list(uploaded.keys())[0]
transcription_file = transcribe(audio_file)
# Download the transcription file
files.download(transcription_file)
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