initial commit

app.py

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+import streamlit as st
+from transformers import WhisperForConditionalGeneration, WhisperTokenizer, WhisperProcessor
+import torchaudio
+import torch
+import io
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+model_path = "Danyal-AI/whisper-small-FT_on_lahaja"
+tokenizer = WhisperTokenizer.from_pretrained(model_path, language="Hindi", task="transcribe")
+feature_extractor = WhisperProcessor.from_pretrained(model_path, language="Hindi", task="transcribe")
+model = WhisperForConditionalGeneration.from_pretrained(model_path).to(device)
+
+def transcribe_audio(audio_bytes):
+    audio_stream = io.BytesIO(audio_bytes)
+    speech_array, sr = torchaudio.load(audio_stream)
+    resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000)
+    speech_array = resampler(speech_array)
+    input_features = feature_extractor(speech_array.squeeze(0), sampling_rate=16000).input_features
+    inputs = {"input_features": torch.tensor(input_features).to(device)}
+    generated_ids = model.generate(**inputs)
+    transcription = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+    return transcription[0]
+
+st.title("Hindi Speech Transcription")
+st.write("Upload an audio file to transcribe Hindi speech.")
+
+uploaded_file = st.file_uploader("Choose an audio file...", type=["wav", "mp3", "m4a"])
+
+if uploaded_file is not None:
+
+    audio_bytes = uploaded_file.read()
+    transcription = transcribe_audio(audio_bytes)
+    st.write("Transcription:")
+    st.write(transcription)

requirements.txt

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+streamlit
+torchaudio
+torch
+transformers
+ffmpeg-python