Update app.py

app.py

@@ -1,89 +1,158 @@
 #!/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)
+
+Hinweis: Verwende diese App nur für eigene oder freigegebene Inhalte.
+"""
+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
-
-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
+    import whisper
+except Exception:
+    whisper = None
+
+def run_capture(cmd):
+    result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+    if result.returncode != 0:
+        err_tail = result.stderr[-1000:] if result.stderr else ""
+        raise RuntimeError(f"Command failed: {' '.join(cmd)}\n{err_tail}")
+    return result.stdout
+
+def download_video_with_ytdlp(url, out_dir, cookies_path=None, format_selector=None):
+    out_template = str(Path(out_dir) / "%(title)s.%(ext)s")
+    cmd = ["yt-dlp", "-o", out_template]
+    if format_selector:
+        cmd += ["-f", format_selector]
+    if cookies_path:
+        cmd += ["--cookies", cookies_path]
+    cmd.append(url)
+    run_capture(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, out_wav):
+    cmd = ["ffmpeg", "-y", "-i", video_path, "-vn", "-ac", "1", "-ar", "16000", "-f", "wav", out_wav]
+    subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+    return out_wav
+
+def seconds_to_timestamp(s):
+    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):
+    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):
+    parts = []
+    for i, seg in enumerate(segments, start=1):
+        start = seconds_to_timestamp(seg['start'])
+        end = seconds_to_timestamp(seg['end'])
+        text = seg['text'].strip()
+        parts.append(f"{i}\n{start} --> {end}\n{text}\n")
+    return "\n".join(parts)
+
+def segments_to_vtt(segments):
+    parts = ["WEBVTT\n"]
+    for seg in segments:
+        start = format_timestamp_vtt(seg['start'])
+        end = format_timestamp_vtt(seg['end'])
+        text = seg['text'].strip()
+        parts.append(f"{start} --> {end}\n{text}\n")
+    return "\n".join(parts)
+
+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, cookies_file=None, format_selector=None):
+    if whisper is None:
+        return "Fehler: whisper ist nicht installiert.", None, None, None, None, None
+    tmpdir = tempfile.mkdtemp(prefix="whisper_space_")
+    try:
+        if url:
+            cookies_path = cookies_file if cookies_file and os.path.exists(cookies_file) else None
+            video_path = download_video_with_ytdlp(url, tmpdir, cookies_path=cookies_path, format_selector=format_selector)
+        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)
         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)
-    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)))
-
+            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})
+        base = Path(video_path).stem
+        files = {}
+        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")
+            files[ext] = str(p)
+        if not keep_video and url:
+            try:
+                os.remove(video_path)
+            except Exception:
+                pass
+        return txt_text, files["srt"], files["vtt"], files["txt"], files["json"], f"Model: {model_size}, Sprache: {language}"
+    except Exception as e:
+        return f"Fehler: {e}", None, None, None, None, None
+
+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")
+            cookies_in = gr.File(label="Cookies.txt (optional)")
+            fmt_in = gr.Textbox(label="Format (optional, yt-dlp -f)")
+            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", 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, c, fmt):
+        cookies_path = c if isinstance(c, str) and os.path.exists(c) else None
+        display, srtf, vttf, txtf, jsonf, meta = transcribe_pipeline(f, u, m, k, cookies_file=cookies_path, format_selector=fmt)
+        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, cookies_in, fmt_in], [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)))