Delete transcribe.py

transcribe.py

@@ -1,91 +0,0 @@
-import os
-
-import numpy as np
-import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, WhisperFeatureExtractor
-from moviepy.editor import VideoFileClip, AudioFileClip
-import nltk
-nltk.download('punkt', quiet=True)
-from nltk.tokenize import sent_tokenize
-import librosa
-
-def transcribe(video_file, transcribe_to_text=True, transcribe_to_srt=True, target_language='en'):
-    device = "cuda:0" if torch.cuda.is_available() else "cpu"
-    torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-    model_id = "openai/whisper-large-v3"
-    model = AutoModelForSpeechSeq2Seq.from_pretrained(
-        model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
-    )
-    model.to(device)
-
-    processor = AutoProcessor.from_pretrained(model_id)
-    feature_extractor = WhisperFeatureExtractor.from_pretrained(model_id)
-
-    video = VideoFileClip(video_file)
-    audio = video.audio
-    duration = audio.duration
-    chunk_duration = 60
-    n_chunks = int(np.ceil(duration / chunk_duration))
-
-    full_transcription = ""
-    for i in range(n_chunks):
-        start_time = i * chunk_duration
-        end_time = min((i + 1) * chunk_duration, duration)
-
-        audio_chunk = audio.subclip(start_time, end_time)
-        temp_file_path = f"temp_audio_chunk_{i}.wav"
-        audio_chunk.write_audiofile(temp_file_path, codec='pcm_s16le')
-
-
-        try:
-            sound_array, _ = librosa.load(temp_file_path, sr=16000)
-        except Exception as e:
-            print(f"Error reading audio file: {e}")
-            continue  # Skip this chunk if there's an error
-    
-        
-        if sound_array.ndim > 1:
-            sound_array = np.mean(sound_array, axis=1)
-
-        input_features = feature_extractor(sound_array, sampling_rate=16000, return_tensors="pt").input_features
-        input_features = input_features.to(device=device, dtype=torch_dtype)
-
-        with torch.no_grad():
-            if target_language:
-                model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language=target_language,
-                                                                                   task="transcribe")
-            generated_ids = model.generate(input_features, max_length=448)
-            transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
-        full_transcription += transcription + " "
-        os.remove(temp_file_path)
-        print(f"Processed chunk {i + 1}/{n_chunks}")
-
-    # Split the transcription into sentences
-    sentences = sent_tokenize(full_transcription.strip())
-
-    # Estimate time for each sentence based on its length relative to the total transcription
-    total_chars = sum(len(s) for s in sentences)
-    sentence_times = []
-    current_time = 0
-    for sentence in sentences:
-        sentence_duration = (len(sentence) / total_chars) * duration
-        sentence_times.append((current_time, current_time + sentence_duration))
-        current_time += sentence_duration
-
-    output = ""
-    if transcribe_to_text:
-        output += "Text Transcription:\n" + full_transcription + "\n\n"
-
-    if transcribe_to_srt:
-        output += "SRT Transcription:\n"
-        for i, (sentence, (start, end)) in enumerate(zip(sentences, sentence_times), 1):
-            output += f"{i}\n{format_time(start)} --> {format_time(end)}\n{sentence}\n\n"
-
-    return output
-
-def format_time(seconds):
-    m, s = divmod(seconds, 60)
-    h, m = divmod(m, 60)
-    return f"{int(h):02d}:{int(m):02d}:{s:06.3f}".replace('.', ',')