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daf9786 fda2fcb daf9786 | 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 | 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"
model_path= "Danyal-AI/Whisper-FT_on_indicVoices"
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)
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