File size: 2,411 Bytes
82e14f1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
# Load model directly
import streamlit as st
import torch
import numpy as np
import librosa
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq, pipeline
from io import BytesIO
# Configure Streamlit page settings
st.set_page_config(
page_title="Transcribe with Whisper",
page_icon=":rocket:",
layout="centered"
)
st.title("🎙️ Whisper Audio Transcriber")
st.divider()
# Set up device and data type
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
# Load the Whisper model and processor
with st.spinner("🚀 Loading Whisper model... please wait!"):
model_name = "openai/whisper-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_name,
torch_dtype=torch_dtype,
low_cpu_mem_usage=True,
use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_name)
# Initialize ASR pipeline
asr_pipe = pipeline(
task="automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=torch_dtype,
device=device,
)
st.markdown("Upload your audio files, and let the Whisper model transcribe them instantly. 🚀")
# File uploader for audio files
uploaded_files = st.file_uploader("📂 Select audio files to transcribe", type=["wav","mp3"], accept_multiple_files=True)
# Transcription button and result display
if uploaded_files:
if st.button("✍️ Transcribe"):
results = []
for idx, audio_file in enumerate(uploaded_files):
try:
# Read audio file
audio_data, sr = librosa.load(BytesIO(audio_file.read()), sr=16000)
# Run ASR pipeline
result = asr_pipe(audio_data)
transcription = result['text']
results.append((audio_file.name, transcription))
except Exception as e:
st.error(f"Error processing '{audio_file.name}':{e}")
# Display results
st.subheader("Transcriptions")
for filename, transcription in results:
st.text_area(f"📂 **{filename}**:", value=transcription)
else:
st.info("📤 Please upload audio files to start transcription.") |