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import streamlit as st
# from transformers import AutoModelForSpeechSeq2Seq, Wav2Vec2Processor
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
import torch
import tempfile
from pydub import AudioSegment
import numpy as np
# Define available models
# available_models = [
# "facebook/s2t-small-mustc-en-fr-st",
# "facebook/s2t-medium-mustc-en-fr-st",
# "facebook/s2t-large-mustc-en-fr-st"
# ]
available_models = ["Yehor/whisper-small-ukrainian"]
st.title("Voice Recognition App using SpeechSeq2Seq")
st.write("Upload an audio file and choose a model to transcribe it to text.")
# Model selection dropdown
model_choice = st.selectbox("Choose a SpeechSeq2Seq model", available_models)
# Load the selected model and processor
@st.cache_resource
def load_model_and_processor(model_name):
# model = AutoModelForSpeechSeq2Seq.from_pretrained(model_name)
# processor = Wav2Vec2Processor.from_pretrained(model_name)
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_name)
processor = AutoProcessor.from_pretrained(model_name)
return model, processor
st.write(f"Loading {model_choice} model...")
model, processor = load_model_and_processor(model_choice)
st.write(f"{model_choice} model loaded successfully.")
# File uploader for audio file
uploaded_file = st.file_uploader("Choose an audio file", type=["wav", "mp3", "m4a"])
if uploaded_file is not None:
# Save the uploaded file temporarily
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
temp_file.write(uploaded_file.read())
temp_file_path = temp_file.name
# Convert audio file to a format supported by the processor (if necessary)
audio = AudioSegment.from_file(temp_file_path)
temp_wav_path = tempfile.mktemp(suffix=".wav")
audio.export(temp_wav_path, format="wav")
st.audio(uploaded_file, format="audio/wav")
st.write("Transcribing audio...")
# # Load audio
# audio_input = AudioSegment.from_file(temp_wav_path).set_frame_rate(16000).set_channels(1)
# audio_input = np.array(audio_input.get_array_of_samples())
#
# # Normalize audio
# audio_input = (audio_input - np.mean(audio_input)) / np.std(audio_input)
#
# # Process the audio
# input_features = processor(audio_input, return_tensors="pt", sampling_rate=16000).input_values
#
# # Generate transcription
# with torch.no_grad():
# predicted_ids = model.generate(input_features)
#
# transcription = processor.batch_decode(predicted_ids)[0]
transcription = model.transcribe(temp_wav_path)
st.write("Transcription:")
st.write(transcription)
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