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| import streamlit as st | |
| from transformers import AutoProcessor, SeamlessM4Tv2Model | |
| import torchaudio | |
| import soundfile as sf | |
| import torch | |
| import os | |
| language_map = { | |
| "Modern Standard Arabic": "arb", "Bengali": "ben", "Catalan": "cat", | |
| "Czech": "ces", "Mandarin Chinese": "cmn", "Welsh": "cym", | |
| "Danish": "dan", "German": "deu", "English": "eng", | |
| "Estonian": "est", "Finnish": "fin", "French": "fra", | |
| "Hindi": "hin", "Indonesian": "ind", "Italian": "ita", | |
| "Japanese": "jpn", "Kannada": "kan", "Korean": "kor", | |
| "Maltese": "mlt", "Dutch": "nld", "Western Persian": "pes", | |
| "Polish": "pol", "Portuguese": "por", "Romanian": "ron", | |
| "Russian": "rus", "Slovak": "slk", "Spanish": "spa", | |
| "Swedish": "swe", "Swahili": "swh", "Tamil": "tam", | |
| "Telugu": "tel", "Tagalog": "tgl", "Thai": "tha", | |
| "Turkish": "tur", "Ukrainian": "ukr", "Urdu": "urd", | |
| "Northern Uzbek": "uzn", "Vietnamese": "vie" | |
| } | |
| # Check if CUDA (GPU support) is available and set the device accordingly | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Function to load and cache the model and processor | |
| def load_model_and_processor(): | |
| processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large") | |
| model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large") | |
| model.to(device) | |
| return processor, model | |
| processor, model = load_model_and_processor() | |
| # Streamlit app layout | |
| st.title("Sarvm Audio Search") | |
| # Sidebar components | |
| st.sidebar.header("Input Settings") | |
| input_text = st.sidebar.text_input("Enter text for conversion:", "Hello, my dog is cute") | |
| selected_language_name = st.sidebar.selectbox("Select Target Language:", list(language_map.keys())) | |
| selected_language_code = language_map[selected_language_name] | |
| # Function to convert text to audio and save | |
| def text_to_audio(text, language): | |
| text_inputs = processor(text=text, src_lang="eng", return_tensors="pt").to(device) | |
| audio_array = model.generate(**text_inputs, tgt_lang=language)[0].cpu().numpy().squeeze() | |
| file_path = 'audio_from_text.wav' | |
| sf.write(file_path, audio_array, 16000) | |
| return file_path | |
| # Function to convert audio to audio and save | |
| def audio_to_audio(input_audio_path, language): | |
| audio, orig_freq = torchaudio.load(input_audio_path) | |
| audio = torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16000).to(device) | |
| audio_inputs = processor(audios=audio, return_tensors="pt").to(device) | |
| audio_array = model.generate(**audio_inputs, tgt_lang=language)[0].cpu().numpy().squeeze() | |
| file_path = 'audio_from_audio.wav' | |
| sf.write(file_path, audio_array, 16000) | |
| return file_path | |
| # UI to trigger text-to-audio conversion | |
| if st.sidebar.button("Convert Text to Audio"): | |
| with st.spinner("Converting..."): | |
| audio_path = text_to_audio(input_text, selected_language_code) | |
| st.audio(audio_path) | |
| # UI to upload audio file and trigger audio-to-audio conversion | |
| uploaded_audio = st.sidebar.file_uploader("Upload audio for conversion:", type=["wav"]) | |
| if uploaded_audio is not None and st.button("Convert Uploaded Audio"): | |
| with st.spinner("Converting..."): | |
| audio_file_path = f"temp_{uploaded_audio.name}" | |
| with open(audio_file_path, "wb") as f: | |
| f.write(uploaded_audio.getvalue()) | |
| converted_audio_path = audio_to_audio(audio_file_path, selected_language_code) | |
| st.audio(converted_audio_path) | |
| os.remove(audio_file_path) |