Update app.py

app.py

@@ -1,161 +1,89 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Hugging Face Space (Gradio) App: Video -> Audio -> Whisper Transkript (+ Downloads SRT/TXT/VTT/JSON)
-"""
-import os
-import subprocess
-import tempfile
-import json
-from pathlib import Path
-from datetime import timedelta
+
+-- coding: utf-8 --
+
+""" Hugging Face Space (Gradio) App: Video -> Audio -> Whisper Transkript (+ Downloads SRT/TXT/VTT/JSON) """ import os import subprocess import tempfile import json from pathlib import Path from datetime import timedelta
 
 import gradio as gr
 
-try:
-    import whisper
-except Exception:
-    whisper = None
+try: import whisper except Exception: whisper = None
 
-def run(cmd, hide_output=False):
-    if hide_output:
-        subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
-    else:
-        subprocess.run(cmd, check=True)
-
-def download_video_with_ytdlp(url: str, out_dir: str) -> str:
-    out_template = str(Path(out_dir) / "%(title)s.%(ext)s")
-    cmd = ["yt-dlp", "-f", "best", "-o", out_template, url]
-    run(cmd)
-    files = sorted(Path(out_dir).glob("*"), key=lambda p: p.stat().st_mtime, reverse=True)
-    if not files:
-        raise FileNotFoundError("Download fehlgeschlagen — keine Datei gefunden.")
-    return str(files[0])
-
-def extract_audio_ffmpeg(video_path: str, out_wav: str):
-    cmd = [
-        "ffmpeg", "-y", "-i", video_path,
-        "-vn", "-ac", "1", "-ar", "16000", "-f", "wav", out_wav
-    ]
-    run(cmd, hide_output=True)
-    return out_wav
-
-def seconds_to_timestamp(s: float) -> str:
-    hours = int(s // 3600)
-    minutes = int((s % 3600) // 60)
-    seconds = int(s % 60)
-    ms = int(round((s - int(s)) * 1000))
-    return f"{hours:02d}:{minutes:02d}:{seconds:02d},{ms:03d}"
-
-def format_timestamp_vtt(s: float) -> str:
-    hours = int(s // 3600)
-    minutes = int((s % 3600) // 60)
-    seconds = int(s % 60)
-    ms = int(round((s - int(s)) * 1000))
-    return f"{hours:02d}:{minutes:02d}:{seconds:02d}.{ms:03d}"
-
-def segments_to_srt(segments):
-    out = []
-    for i, seg in enumerate(segments, start=1):
-        start = seconds_to_timestamp(seg['start'])
-        end = seconds_to_timestamp(seg['end'])
-        text = seg['text'].strip()
-        out.append(f"{i}\n{start} --> {end}\n{text}\n")
-    return "\n".join(out)
-
-def segments_to_vtt(segments):
-    out = ["WEBVTT\n"]
-    for seg in segments:
-        start = format_timestamp_vtt(seg['start'])
-        end = format_timestamp_vtt(seg['end'])
-        text = seg['text'].strip()
-        out.append(f"{start} --> {end}\n{text}\n")
-    return "\n".join(out)
-
-def segments_to_txt(segments):
-    return "\n".join([f"[{seconds_to_timestamp(seg['start'])}] {seg['text'].strip()}" for seg in segments])
-
-def segments_to_json(segments, language=None, metadata=None):
-    data = {"language": language, "segments": segments}
-    if metadata:
-        data["metadata"] = metadata
-    return json.dumps(data, ensure_ascii=False, indent=2)
-
-def transcribe_pipeline(file_obj, url, model_size, keep_video=False):
-    if whisper is None:
-        return "Fehler: whisper ist nicht installiert.", None, None, None, None, None
-
-    tmpdir = tempfile.mkdtemp(prefix="whisper_space_")
-    try:
-        if url:
-            video_path = download_video_with_ytdlp(url, tmpdir)
-        elif file_obj:
-            if isinstance(file_obj, str) and os.path.exists(file_obj):
-                video_path = file_obj
-            else:
-                uploaded_path = Path(tmpdir) / Path(getattr(file_obj, "name", "upload")).name
-                with open(uploaded_path, "wb") as f:
-                    f.write(file_obj.read())
-                video_path = str(uploaded_path)
+def run(cmd, hide_output=False): if hide_output: subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) else: subprocess.run(cmd, check=True)
+
+def download_video_with_ytdlp(url: str, out_dir: str) -> str: out_template = str(Path(out_dir) / "%(title)s.%(ext)s") cmd = ["yt-dlp", "-f", "best", "-o", out_template, url] run(cmd) files = sorted(Path(out_dir).glob("*"), key=lambda p: p.stat().st_mtime, reverse=True) if not files: raise FileNotFoundError("Download fehlgeschlagen — keine Datei gefunden.") return str(files[0])
+
+def extract_audio_ffmpeg(video_path: str, out_wav: str): cmd = [ "ffmpeg", "-y", "-i", video_path, "-vn", "-ac", "1", "-ar", "16000", "-f", "wav", out_wav ] run(cmd, hide_output=True) return out_wav
+
+def seconds_to_timestamp(s: float) -> str: hours = int(s // 3600) minutes = int((s % 3600) // 60) seconds = int(s % 60) ms = int(round((s - int(s)) * 1000)) return f"{hours:02d}:{minutes:02d}:{seconds:02d},{ms:03d}"
+
+def format_timestamp_vtt(s: float) -> str: hours = int(s // 3600) minutes = int((s % 3600) // 60) seconds = int(s % 60) ms = int(round((s - int(s)) * 1000)) return f"{hours:02d}:{minutes:02d}:{seconds:02d}.{ms:03d}"
+
+def segments_to_srt(segments): out = [] for i, seg in enumerate(segments, start=1): start = seconds_to_timestamp(seg['start']) end = seconds_to_timestamp(seg['end']) text = seg['text'].strip() out.append(f"{i}\n{start} --> {end}\n{text}\n") return "\n".join(out)
+
+def segments_to_vtt(segments): out = ["WEBVTT\n"] for seg in segments: start = format_timestamp_vtt(seg['start']) end = format_timestamp_vtt(seg['end']) text = seg['text'].strip() out.append(f"{start} --> {end}\n{text}\n") return "\n".join(out)
+
+def segments_to_txt(segments): return "\n".join([f"[{seconds_to_timestamp(seg['start'])}] {seg['text'].strip()}" for seg in segments])
+
+def segments_to_json(segments, language=None, metadata=None): data = {"language": language, "segments": segments} if metadata: data["metadata"] = metadata return json.dumps(data, ensure_ascii=False, indent=2)
+
+def transcribe_pipeline(file_obj, url, model_size, keep_video=False): if whisper is None: return "Fehler: whisper ist nicht installiert.", None, None, None, None, None
+
+tmpdir = tempfile.mkdtemp(prefix="whisper_space_")
+try:
+    if url:
+        video_path = download_video_with_ytdlp(url, tmpdir)
+    elif file_obj:
+        if isinstance(file_obj, str) and os.path.exists(file_obj):
+            video_path = file_obj
         else:
-            return "Kein Video angegeben.", None, None, None, None, None
-
-        audio_wav = str(Path(tmpdir) / "audio.wav")
-        extract_audio_ffmpeg(video_path, audio_wav)
-
-        model = whisper.load_model(model_size)
-        result = model.transcribe(audio_wav, verbose=False)
-        segments = result.get("segments", [])
-        language = result.get("language", "unknown")
-
-        srt_text = segments_to_srt(segments)
-        vtt_text = segments_to_vtt(segments)
-        txt_text = segments_to_txt(segments)
-        json_text = segments_to_json(segments, language, {"model": model_size})
-
-        out_files = {}
-        base = Path(video_path).stem
-        for ext, content in {"srt": srt_text, "vtt": vtt_text, "txt": txt_text, "json": json_text}.items():
-            p = Path(tmpdir) / f"{base}.{ext}"
-            p.write_text(content, encoding="utf-8")
-            out_files[ext] = str(p)
-
-        display_text = txt_text
-
-        if not keep_video and url:
-            try:
-                os.remove(video_path)
-            except Exception:
-                pass
-
-        return display_text, out_files["srt"], out_files["vtt"], out_files["txt"], out_files["json"], f"Model: {model_size}, Sprache: {language}"
-    except Exception as e:
-        return f"Fehler: {e}", None, None, None, None, None
-    finally:
-        pass
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Video → Whisper Transkript (SRT/TXT/VTT/JSON)")
-    with gr.Row():
-        with gr.Column():
-            url_in = gr.Textbox(label="Video URL", placeholder="https://...")
-            file_in = gr.File(label="Oder Videodatei hochladen")
-            model_sel = gr.Radio(["tiny", "base", "small", "medium", "large"], value="small", label="Whisper-Modell")
-            keep_chk = gr.Checkbox(label="Video behalten", value=False)
-            btn = gr.Button("Transkribieren")
-            status = gr.Textbox(label="Status")
-        with gr.Column():
-            transcript = gr.Textbox(label="Transkript mit Zeitmarken", lines=20)
-            srt_dl = gr.File(label="SRT", visible=False)
-            vtt_dl = gr.File(label="VTT", visible=False)
-            txt_dl = gr.File(label="TXT", visible=False)
-            json_dl = gr.File(label="JSON", visible=False)
-
-    def run_transcribe(f, u, m, k):
-        display, srtf, vttf, txtf, jsonf, meta = transcribe_pipeline(f, u, m, k)
-        return display, gr.update(value=srtf, visible=bool(srtf)), gr.update(value=vttf, visible=bool(vttf)), gr.update(value=txtf, visible=bool(txtf)), gr.update(value=jsonf, visible=bool(jsonf)), meta
-
-    btn.click(run_transcribe, [file_in, url_in, model_sel, keep_chk], [transcript, srt_dl, vtt_dl, txt_dl, json_dl, status])
-
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
+            uploaded_path = Path(tmpdir) / Path(getattr(file_obj, "name", "upload")).name
+            with open(uploaded_path, "wb") as f:
+                f.write(file_obj.read())
+            video_path = str(uploaded_path)
+    else:
+        return "Kein Video angegeben.", None, None, None, None, None
+
+    audio_wav = str(Path(tmpdir) / "audio.wav")
+    extract_audio_ffmpeg(video_path, audio_wav)
+
+    model = whisper.load_model(model_size)
+    result = model.transcribe(audio_wav, verbose=False)
+    segments = result.get("segments", [])
+    language = result.get("language", "unknown")
+
+    srt_text = segments_to_srt(segments)
+    vtt_text = segments_to_vtt(segments)
+    txt_text = segments_to_txt(segments)
+    json_text = segments_to_json(segments, language, {"model": model_size})
+
+    out_files = {}
+    base = Path(video_path).stem
+    for ext, content in {"srt": srt_text, "vtt": vtt_text, "txt": txt_text, "json": json_text}.items():
+        p = Path(tmpdir) / f"{base}.{ext}"
+        p.write_text(content, encoding="utf-8")
+        out_files[ext] = str(p)
+
+    display_text = txt_text
+
+    if not keep_video and url:
+        try:
+            os.remove(video_path)
+        except Exception:
+            pass
+
+    return display_text, out_files["srt"], out_files["vtt"], out_files["txt"], out_files["json"], f"Model: {model_size}, Sprache: {language}"
+except Exception as e:
+    return f"Fehler: {e}", None, None, None, None, None
+finally:
+    pass
+
+with gr.Blocks() as demo: gr.Markdown("# Video → Whisper Transkript (SRT/TXT/VTT/JSON)") with gr.Row(): with gr.Column(): url_in = gr.Textbox(label="Video URL", placeholder="https://...") file_in = gr.File(label="Oder Videodatei hochladen") model_sel = gr.Radio(["tiny", "base", "small", "medium", "large"], value="small", label="Whisper-Modell") keep_chk = gr.Checkbox(label="Video behalten", value=False) btn = gr.Button("Transkribieren") status = gr.Textbox(label="Status") with gr.Column(): transcript = gr.Textbox(label="Transkript mit Zeitmarken", lines=20) srt_dl = gr.File(label="SRT", visible=False) vtt_dl = gr.File(label="VTT", visible=False) txt_dl = gr.File(label="TXT", visible=False) json_dl = gr.File(label="JSON", visible=False)
+
+def run_transcribe(f, u, m, k):
+    display, srtf, vttf, txtf, jsonf, meta = transcribe_pipeline(f, u, m, k)
+    return display, gr.update(value=srtf, visible=bool(srtf)), gr.update(value=vttf, visible=bool(vttf)), gr.update(value=txtf, visible=bool(txtf)), gr.update(value=jsonf, visible=bool(jsonf)), meta
+
+btn.click(run_transcribe, [file_in, url_in, model_sel, keep_chk], [transcript, srt_dl, vtt_dl, txt_dl, json_dl, status])
+
+if name == "main": demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
+