Create app.py

app.py

@@ -0,0 +1,95 @@
+import os
+import math
+import gradio as gr
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+from moviepy.editor import AudioFileClip
+
+def transcribe(audio_file, transcribe_to_text, transcribe_to_srt, language):
+    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)
+
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model=model,
+        tokenizer=processor.tokenizer,
+        feature_extractor=processor.feature_extractor,
+        max_new_tokens=128,
+        chunk_length_s=30,
+        batch_size=2,
+        return_timestamps=True,
+        torch_dtype=torch_dtype,
+        device=device,
+        generate_kwargs={"language": language}
+    )
+
+    audio = AudioFileClip(audio_file.name)
+    duration = audio.duration
+    n_chunks = math.ceil(duration / 30)
+
+    transcription_txt = ""
+    transcription_srt = []
+
+    for i in range(n_chunks):
+        start = i * 30
+        end = min((i + 1) * 30, duration)
+        audio_chunk = audio.subclip(start, end)
+        
+        temp_file_path = f"temp_audio_{i}.wav"
+        audio_chunk.write_audiofile(temp_file_path, codec='pcm_s16le')
+
+        with open(temp_file_path, "rb") as temp_file:
+            result = pipe(temp_file_path)
+            transcription_txt += result["text"]
+
+            if transcribe_to_srt:
+                for chunk in result["chunks"]:
+                    start_time, end_time = chunk["timestamp"]
+                    transcription_srt.append({
+                        "start": start_time + i * 30,
+                        "end": end_time + i * 30,
+                        "text": chunk["text"]
+                    })
+
+        os.remove(temp_file_path)
+
+        yield f"Progress: {int(((i + 1) / n_chunks) * 100)}%"
+
+    output = ""
+    if transcribe_to_text:
+        output += "Text Transcription:\n" + transcription_txt + "\n\n"
+
+    if transcribe_to_srt:
+        output += "SRT Transcription:\n"
+        for i, sub in enumerate(transcription_srt, 1):
+            output += f"{i}\n{format_time(sub['start'])} --> {format_time(sub['end'])}\n{sub['text']}\n\n"
+
+    yield 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('.', ',')
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(type="filepath"),
+        gr.Checkbox(label="Transcribe to Text"),
+        gr.Checkbox(label="Transcribe to SRT"),
+        gr.Dropdown(choices=['en', 'he', 'it', 'fr', 'de', 'zh', 'ar'], label="Language")
+    ],
+    outputs="text",
+    title="WhisperCap Transcription",
+    description="Upload an audio file to transcribe it using Whisper.",
+)
+
+iface.launch()