Create app.py

app.py

@@ -0,0 +1,65 @@
+import pathlib
+from faster_whisper import WhisperModel
+import yt_dlp
+import uuid
+import os
+import gradio as gr
+
+
+# List of all supported video sites here https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md
+def download_convert_video_to_audio(
+    yt_dlp,
+    video_url: str,
+    destination_path: pathlib.Path,
+) -> None:
+    ydl_opts = {
+        "format": "bestaudio/best",
+        "postprocessors": [
+            {  # Extract audio using ffmpeg
+                "key": "FFmpegExtractAudio",
+                "preferredcodec": "mp3",
+            }
+        ],
+        "outtmpl": f"{destination_path}.%(ext)s",
+    }
+    try:
+        print(f"Downloading video from {video_url}")
+        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+            ydl.download(video_url)
+        print(f"Downloaded video from {video_url} to {destination_path}")
+    except Exception as e:
+        raise (e)
+
+def segment_to_dict(segment):
+    segment = segment._asdict()
+    if segment["words"] is not None:
+        segment["words"] = [word._asdict() for word in segment["words"]]
+    return segment
+
+def download_video(video_url: str):
+    download_convert_video_to_audio(yt_dlp, video_url, f"/content/{uuid.uuid4().hex}")
+
+def transcribe_video(video_url: str, beam_size: int = 5, model_size: str = "tiny", word_timestamps: bool = True):
+    print("loading model")
+    model = WhisperModel(model_size, device="cpu", compute_type="int8")
+    print("getting hex")
+    rand_id = uuid.uuid4().hex
+    print("doing download")
+    download_convert_video_to_audio(yt_dlp, video_url, f"/content/{rand_id}")
+    segments, info = model.transcribe(f"/content/{rand_id}.mp3", beam_size=beam_size, word_timestamps=word_timestamps)
+    segments = [segment_to_dict(segment) for segment in segments]
+    total_duration = round(info.duration, 2)  # Same precision as the Whisper timestamps.
+    print(info)
+    os.remove(f"/content/{rand_id}.mp3")
+    print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
+    print(segments)
+    return segments
+
+# print("Detected language '%s' with probability %f" % (info.language, info.language_probability))
+
+# for segment in segments:
+#     print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
+
+demo = gr.Interface(fn=transcribe_video, inputs="text", outputs="text")
+
+demo.launch()