app.py

import torch
from transformers import pipeline

# Initialize the speech-to-text pipeline from Hugging Face Transformers
# This uses the "openai/whisper-tiny.en" model for automatic speech recognition (ASR)
# The `chunk_length_s` parameter specifies the chunk length in seconds for processing
pipe = pipeline(
  "automatic-speech-recognition",
  model="openai/whisper-tiny.en",
  chunk_length_s=30,
)

# Define the path to the audio file that needs to be transcribed
sample = 'downloaded_audio.mp3'

# Perform speech recognition on the audio file
# The `batch_size=8` parameter indicates how many chunks are processed at a time
# The result is stored in `prediction` with the key "text" containing the transcribed text
prediction = pipe(sample, batch_size=8)["text"]

# Print the transcribed text to the console
print(prediction)