Upload 2 files

app.py

@@ -0,0 +1,85 @@
+import streamlit as st
+from transformers import WhisperProcessor, WhisperForConditionalGeneration, RagTokenizer, RagRetriever, RagSequenceForGeneration
+import torch
+import soundfile as sf
+import librosa
+from moviepy.editor import VideoFileClip
+import os
+
+# Load Whisper base model and processor
+whisper_model_name = "openai/whisper-base"
+whisper_processor = WhisperProcessor.from_pretrained(whisper_model_name)
+whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_name)
+
+# Load RAG sequence model and tokenizer
+rag_model_name = "facebook/rag-sequence-nq"
+rag_tokenizer = RagTokenizer.from_pretrained(rag_model_name)
+rag_retriever = RagRetriever.from_pretrained(rag_model_name, index_name="exact", use_dummy_dataset=True, trust_remote_code=True)
+rag_model = RagSequenceForGeneration.from_pretrained(rag_model_name, retriever=rag_retriever)
+
+def transcribe_audio(audio_path, language="ru"):
+    speech, rate = librosa.load(audio_path, sr=16000)
+    inputs = whisper_processor(speech, return_tensors="pt", sampling_rate=16000)
+    input_features = whisper_processor.feature_extractor(speech, return_tensors="pt", sampling_rate=16000).input_features
+    predicted_ids = whisper_model.generate(input_features, forced_decoder_ids=whisper_processor.get_decoder_prompt_ids(language=language, task="translate"))
+    transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+    return transcription
+
+def translate_and_summarize(text):
+    inputs = rag_tokenizer(text, return_tensors="pt")
+    input_ids = inputs["input_ids"]
+    attention_mask = inputs["attention_mask"]
+    outputs = rag_model.generate(input_ids=input_ids, attention_mask=attention_mask)
+    return rag_tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+def extract_audio_from_video(video_path, output_audio_path):
+    video_clip = VideoFileClip(video_path)
+    audio_clip = video_clip.audio
+    if audio_clip is not None:
+        audio_clip.write_audiofile(output_audio_path)
+        return output_audio_path
+    else:
+        return None
+
+st.title("Audio and Video Transcription & Summarization")
+
+# Audio Upload Section
+st.header("Upload an Audio File")
+audio_file = st.file_uploader("Choose an audio file...", type=["wav", "mp3", "m4a"])
+
+if audio_file is not None:
+    audio_path = os.path.join("/tmp", audio_file.name)
+    with open(audio_path, "wb") as f:
+        f.write(audio_file.getbuffer())
+    
+    st.audio(audio_file)
+    st.write("Transcribing audio...")
+    transcription = transcribe_audio(audio_path)
+    st.write("Transcription:", transcription)
+    
+    st.write("Translating and summarizing...")
+    summary = translate_and_summarize(transcription)
+    st.write("Translated Summary:", summary)
+# Video Upload Section
+st.header("Upload a Video File")
+video_file = st.file_uploader("Choose a video file...", type=["mp4", "mkv", "avi", "mov"])
+
+if video_file is not None:
+    video_path = os.path.join("/tmp", video_file.name)
+    with open(video_path, "wb") as f:
+        f.write(video_file.getbuffer())
+    
+    st.video(video_file)
+    st.write("Extracting audio from video...")
+    audio_path = extract_audio_from_video(video_path, "/tmp/extracted_audio.wav")
+    
+    if audio_path is not None:
+        st.write("Transcribing audio...")
+        transcription = transcribe_audio(audio_path)
+        st.write("Transcription:", transcription)
+        
+        st.write("Translating and summarizing...")
+        summary = translate_and_summarize(transcription)
+        st.write("Translated Summary:", summary)
+    else:
+        st.write("No audio track found in the video file.")

requirements.txt

@@ -0,0 +1,8 @@
+faiss-cpu
+torch
+transformers
+soundfile
+librosa
+datasets
+moviepy
+gradio