Delete app-speech-to-text.py

app-speech-to-text.py

@@ -1,41 +0,0 @@
-import torch
-from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel
-import streamlit as st
-from audio_recorder_streamlit import audio_recorder
-import numpy as np
-
-# Function to transcribe audio to text
-def transcribe_audio(audio_bytes):
-    processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
-    model = SpeechEncoderDecoderModel.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
-
-    # Convert bytes to numpy array
-    audio_array = np.frombuffer(audio_bytes, dtype=np.int16)
-    
-    # Cast audio array to double precision and normalize
-    audio_tensor = torch.tensor(audio_array, dtype=torch.float32) / 32768.0
-    
-    input_values = processor(audio_tensor, return_tensors="pt", sampling_rate=16_000).input_values
-    logits = model(input_values).logits
-    predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
-
-    return transcription[0]
-
-# Streamlit app
-st.title("Audio to Text Transcription")
-
-audio_bytes = audio_recorder(pause_threshold=3.0, sample_rate=16_000)
-
-if audio_bytes:
-    st.audio(audio_bytes, format="audio/wav")
-    
-    transcription = transcribe_audio(audio_bytes)
-
-    if transcription:
-        st.write("Transcription:")
-        st.write(transcription)
-    else:
-        st.write("Error: Failed to transcribe audio.")
-else:
-    st.write("No audio recorded.")