Update app.py

app.py

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+You said:
+# app.py
+# Whisper transcription app - HYBRID conversion (pydub + small ffmpeg fallback)
+# Clean, single-version file for Hugging Face Spaces.
+
+import os
+import sys
+import json
+import shutil
+import tempfile
+import subprocess
+import traceback
+import threading
+import re
+from difflib import get_close_matches
+
+# Force unbuffered output so container logs show prints immediately
+os.environ["PYTHONUNBUFFERED"] = "1"
+
+print("DEBUG: app.py bootstrap starting", flush=True)
+
+# Third-party imports (must be installed in the environment)
+try:
+    from docx import Document
+    import whisper
+    import gradio as gr
+    import pyzipper
+    from pydub import AudioSegment
+except Exception as e:
+    print("FATAL: import error for third-party libs:", e, flush=True)
+    traceback.print_exc()
+    raise
+
+print("DEBUG: imports OK", flush=True)
+
+# ---------- Config ----------
+MEMORY_FILE = "memory.json"
+MEMORY_LOCK = threading.Lock()
+MIN_WAV_SIZE = 200  # bytes
+
+# Small ffmpeg fallback grid (hybrid conversion)
+FFMPEG_CANDIDATES = [
+    ("s16le", 16000, 1),
+    ("s16le", 44100, 2),
+    ("pcm_s16le", 16000, 1),
+    ("pcm_s16le", 44100, 2),
+    ("mulaw", 8000, 1),
+]
+# ----------------------------
+
+# ---------- Memory helpers ----------
+def load_memory():
+    try:
+        if os.path.exists(MEMORY_FILE):
+            with open(MEMORY_FILE, "r", encoding="utf-8") as fh:
+                return json.load(fh)
+    except Exception:
+        pass
+    mem = {"words": {}, "phrases": {}}
+    try:
+        with open(MEMORY_FILE, "w", encoding="utf-8") as fh:
+            json.dump(mem, fh, ensure_ascii=False, indent=2)
+    except Exception:
+        pass
+    return mem
+
+
+def save_memory(mem):
+    with MEMORY_LOCK:
+        with open(MEMORY_FILE, "w", encoding="utf-8") as fh:
+            json.dump(mem, fh, ensure_ascii=False, indent=2)
+
+
+memory = load_memory()
+print(
+    "DEBUG: memory loaded (words=%d phrases=%d)"
+    % (len(memory.get("words", {})), len(memory.get("phrases", {}))),
+    flush=True,
+)
+
+# ---------- Postprocessing ----------
+MEDICAL_ABBREVIATIONS = {
+    "pt": "patient",
+    "dx": "diagnosis",
+    "hx": "history",
+    "sx": "symptoms",
+    "c/o": "complains of",
+    "bp": "blood pressure",
+    "hr": "heart rate",
+    "o2": "oxygen",
+    "r/o": "rule out",
+    "adm": "admit",
+    "disch": "discharge",
+}
+
+DRUG_NORMALIZATION = {
+    "metformin": "Metformin",
+    "aspirin": "Aspirin",
+    "amoxicillin": "Amoxicillin",
+}
+
+
+def expand_abbreviations(text):
+    tokens = re.split(r"(\s+)", text)
+    out = []
+    for t in tokens:
+        key = t.lower().strip(".,;:")
+        if key in MEDICAL_ABBREVIATIONS:
+            trailing = ""
+            m = re.match(r"([A-Za-z0-9/]+)([.,;:]*)", t)
+            if m:
+                trailing = m.group(2) or ""
+            out.append(MEDICAL_ABBREVIATIONS[key] + trailing)
+        else:
+            out.append(t)
+    return "".join(out)
+
+
+def normalize_drugs(text):
+    for k, v in DRUG_NORMALIZATION.items():
+        text = re.sub(rf"\b{k}\b", v, text, flags=re.IGNORECASE)
+    return text
+
+
+def punctuation_and_capitalization(text):
+    text = text.strip()
+    if not text:
+        return text
+    if not re.search(r"[.?!]\s*$", text):
+        text = text.rstrip() + "."
+    parts = re.split(r"([.?!]\s+)", text)
+    out = []
+    for p in parts:
+        if p and not re.match(r"[.?!]\s+", p):
+            out.append(p.capitalize())
+        else:
+            out.append(p)
+    return "".join(out)
+
+
+def postprocess_transcript(text, format_soap=False):
+    if not text:
+        return text
+    t = re.sub(r"\s+", " ", text).strip()
+    t = expand_abbreviations(t)
+    t = normalize_drugs(t)
+    t = punctuation_and_capitalization(t)
+    if format_soap:
+        sentences = re.split(r"(?<=[.?!])\s+", t)
+        subj = sentences[0] if len(sentences) >= 1 else ""
+        obj = sentences[1] if len(sentences) >= 2 else ""
+        assessment = ""
+        for kw in ["diagnosis", "dx", "rule out", "r/o", "probable"]:
+            if kw in t.lower():
+                assessment = "Assessment: " + subj
+                break
+        soap = (
+            f"S: {subj}\nO: {obj}\nA: {assessment}\nP: Plan: follow up as indicated."
+        )
+        return soap
+    return t
+
+
+# ---------- Memory utilities ----------
+def extract_words_and_phrases(text):
+    # basic tokenization for words; phrases = sentences
+    words = re.findall(r"[A-Za-z0-9\-']+", text)
+    sentences = [
+        s.strip() for s in re.split(r"(?<=[.?!])\s+", text) if s.strip()
+    ]
+    return [w for w in words if w.strip()], sentences
+
+
+def update_memory_with_transcript(transcript):
+    global memory
+    words, sentences = extract_words_and_phrases(transcript)
+    changed = False
+    with MEMORY_LOCK:
+        for w in words:
+            lw = w.lower()
+            if lw in memory["words"]:
+                memory["words"][lw] += 1
+            else:
+                memory["words"][lw] = 1
+                changed = True
+        for s in sentences:
+            key = s.strip()
+            if key in memory["phrases"]:
+                memory["phrases"][key] += 1
+            else:
+                memory["phrases"][key] = 1
+                changed = True
+        if changed:
+            try:
+                with open(MEMORY_FILE, "w", encoding="utf-8") as fh:
+                    json.dump(memory, fh, ensure_ascii=False, indent=2)
+            except Exception:
+                pass
+
+
+def memory_correct_text(text, min_ratio=0.85):
+    if not text or (not memory.get("words") and not memory.get("phrases")):
+        return text
+
+    def fix_word(w):
+        lw = w.lower()
+        if lw in memory["words"]:
+            return w
+        candidates = get_close_matches(
+            lw, memory["words"].keys(), n=1, cutoff=min_ratio
+        )
+        if candidates:
+            cand = candidates[0]
+            if w and w[0].isupper():
+                return cand.capitalize()
+            return cand
+        return w
+
+    tokens = re.split(r"(\W+)", text)
+    corrected_tokens = []
+    for tok in tokens:
+        if re.match(r"^[A-Za-z0-9\-']+$", tok):
+            corrected_tokens.append(fix_word(tok))
+        else:
+            corrected_tokens.append(tok)
+    corrected = "".join(corrected_tokens)
+
+    for phrase in sorted(memory.get("phrases", {}).keys(), key=lambda s: -len(s)):
+        low_phrase = phrase.lower()
+        if len(low_phrase) < 8:
+            continue
+        if low_phrase in corrected.lower():
+            corrected = re.sub(
+                re.escape(phrase), phrase, corrected, flags=re.IGNORECASE
+            )
+    return corrected
+
+
+# ---------- File utilities ----------
+def save_as_word(text, filename=None):
+    if filename is None:
+        filename = os.path.join(
+            tempfile.gettempdir(), "merged_transcripts.docx"
+        )
+    doc = Document()
+    doc.add_paragraph(text)
+    doc.save(filename)
+    return filename
+
+
+# ---------- Hybrid conversion: pydub + small ffmpeg fallback ----------
+def _ffmpeg_convert(input_path, out_path, fmt, sr, ch):
+    cmd = [
+        "ffmpeg",
+        "-hide_banner",
+        "-loglevel",
+        "error",
+        "-y",
+        "-f",
+        fmt,
+        "-ar",
+        str(sr),
+        "-ac",
+        str(ch),
+        "-i",
+        input_path,
+        out_path,
+    ]
+    try:
+        proc = subprocess.run(cmd, capture_output=True, timeout=30, text=True)
+        if (
+            proc.returncode == 0
+            and os.path.exists(out_path)
+            and os.path.getsize(out_path) > MIN_WAV_SIZE
+        ):
+            return True, proc.stderr + proc.stdout
+        else:
+            try:
+                if os.path.exists(out_path):
+                    os.unlink(out_path)
+            except Exception:
+                pass
+            return False, proc.stderr + proc.stdout
+    except Exception as e:
+        try:
+            if os.path.exists(out_path):
+                os.unlink(out_path)
+        except Exception:
+            pass
+        return False, str(e)
+
+
+def convert_to_wav_if_needed(input_path):
+    input_path = str(input_path)
+    lower = input_path.lower()
+    if lower.endswith(".wav"):
+        return input_path
+
+    auto_err = ""
+    tmp = None
+    try:
+        tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+        tmp.close()
+        AudioSegment.from_file(input_path).export(tmp.name, format="wav")
+        if os.path.exists(tmp.name) and os.path.getsize(tmp.name) > MIN_WAV_SIZE:
+            return tmp.name
+        else:
+            try:
+                os.unlink(tmp.name)
+            except Exception:
+                pass
+    except Exception:
+        auto_err = traceback.format_exc()
+        try:
+            if tmp and os.path.exists(tmp.name):
+                os.unlink(tmp.name)
+        except Exception:
+            pass
+
+    # ffmpeg fallback
+    diag_dir = tempfile.mkdtemp(prefix="dct_diag_")
+    diag_log = os.path.join(diag_dir, "conversion_diagnostics.txt")
+    diagnostics = []
+    for fmt, sr, ch in FFMPEG_CANDIDATES:
+        out_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+        out_wav.close()
+        success, debug = _ffmpeg_convert(input_path, out_wav.name, fmt, sr, ch)
+        diagnostics.append(
+            f"TRY fmt={fmt} sr={sr} ch={ch} success={success}\n{debug}\n"
+        )
+        if success:
+            try:
+                with open(diag_log, "w", encoding="utf-8") as fh:
+                    fh.write("pydub auto error:\n")
+                    fh.write(auto_err + "\n\n")
+                    fh.write("Successful ffmpeg candidate:\n")
+                    fh.write(f"fmt={fmt} sr={sr} ch={ch}\n\n")
+                    fh.write("Diagnostics:\n")
+                    fh.write("\n".join(diagnostics))
+            except Exception:
+                pass
+            return out_wav.name
+        else:
+            try:
+                if os.path.exists(out_wav.name):
+                    os.unlink(out_wav.name)
+            except Exception:
+                pass
+
+    # final diagnostics
+    try:
+        fp = subprocess.run(
+            ["ffprobe", "-v", "error", "-show_format", "-show_streams", input_path],
+            capture_output=True,
+            text=True,
+            timeout=10,
+        )
+        diagnostics.append("FFPROBE:\n" + (fp.stdout.strip() or fp.stderr.strip()))
+    except Exception as e:
+        diagnostics.append("ffprobe failed: " + str(e))
+    try:
+        with open(input_path, "rb") as fh:
+            head = fh.read(512)
+            diagnostics.append("HEX PREVIEW:\n" + head.hex())
+    except Exception as e:
+        diagnostics.append("could not read head: " + str(e))
+
+    try:
+        with open(diag_log, "w", encoding="utf-8") as fh:
+            fh.write("pydub auto error:\n")
+            fh.write(auto_err + "\n\n")
+            fh.write("Full diagnostics:\n\n")
+            fh.write("\n\n".join(diagnostics))
+    except Exception as e:
+        raise Exception(f"Conversion failed; diagnostics write error: {e}")
+
+    raise Exception(
+        f"Could not convert file to WAV. Diagnostics saved to: {diag_log}"
+    )
+
+
+# ---------- Whisper model cache ----------
+MODEL_CACHE = {}
+
+
+def get_whisper_model(name):
+    if name not in MODEL_CACHE:
+        print(f"DEBUG: loading whisper model '{name}'", flush=True)
+        MODEL_CACHE[name] = whisper.load_model(name)
+    return MODEL_CACHE[name]
+
+
+# ---------- Main transcription generator ----------
+def transcribe_multiple(
+    audio_files,
+    model_name,
+    advanced_options,
+    merge_checkbox,
+    zip_file=None,
+    zip_password=None,
+    enable_memory=False,
+):
+    """
+    Generator yields (log_text, transcripts_text, merged_file_path_or_None, percent_int)
+    """
+    log = []
+    transcripts = []
+    word_file_path = None
+    temp_extract_dir = os.path.join(tempfile.gettempdir(), "extracted_audio")
+    extracted_audio_paths = []
+
+    # initial yield
+    yield "", "", None, 0
+
+    # cleanup previous temp dir
+    if os.path.exists(temp_extract_dir):
+        try:
+            shutil.rmtree(temp_extract_dir)
+            log.append(f"Cleaned previous temp dir: {temp_extract_dir}")
+        except Exception:
+            pass
+
+    # handle zip
+    if zip_file:
+        log.append(f"Processing zip: {zip_file}")
+        yield "\n\n".join(log), "\n\n".join(transcripts), None, 2
+        try:
+            os.makedirs(temp_extract_dir, exist_ok=True)
+            with pyzipper.ZipFile(zip_file, "r") as zf:
+                if zip_password:
+                    try:
+                        zf.setpassword(zip_password.encode())
+                    except Exception:
+                        log.append("Incorrect zip password")
+                        yield "\n\n".join(log), "\n\n".join(transcripts), None, 100
+                        return
+                exts = [
+                    ".mp3",
+                    ".wav",
+                    ".aac",
+                    ".flac",
+                    ".ogg",
+                    ".m4a",
+                    ".dat",
+                    ".dct",
+                ]
+                count = 0
+                for info in zf.infolist():
+                    if info.is_dir():
+                        continue
+                    _, ext = os.path.splitext(info.filename)
+                    if ext.lower() in exts:
+                        try:
+                            zf.extract(info, path=temp_extract_dir)
+                        except Exception as e:
+                            log.append(f"Error extracting {info.filename}: {e}")
+                            continue
+                        p = os.path.normpath(
+                            os.path.join(temp_extract_dir, info.filename)
+                        )
+                        if os.path.exists(p):
+                            extracted_audio_paths.append(p)
+                            count += 1
+                            log.append(f"Extracted: {info.filename}")
+                if count == 0:
+                    log.append("No supported audio in zip.")
+                    try:
+                        shutil.rmtree(temp_extract_dir)
+                    except Exception:
+                        pass
+                    yield "\n\n".join(log), "\n\n".join(transcripts), None, 100
+                    return
+        except pyzipper.BadZipFile:
+            log.append("Invalid zip file.")
+            try:
+                shutil.rmtree(temp_extract_dir)
+            except Exception:
+                pass
+            yield "\n\n".join(log), "\n\n".join(transcripts), None, 100
+            return
+        except Exception as e:
+            log.append(f"Zip processing error: {e}")
+            try:
+                shutil.rmtree(temp_extract_dir)
+            except Exception:
+                pass
+            yield "\n\n".join(log), "\n\n".join(transcripts), None, 100
+            return
+
+    # collect audio file paths
+    paths = []
+    if extracted_audio_paths:
+        paths.extend(extracted_audio_paths)
+    if audio_files:
+        if isinstance(audio_files, (list, tuple)):
+            for a in audio_files:
+                if a:
+                    paths.append(a)
+        elif isinstance(audio_files, str):
+            paths.append(audio_files)
+
+    if not paths:
+        log.append("No audio files provided.")
+        yield "\n\n".join(log), "\n\n".join(transcripts), None, 100
+        return
+
+    # load model
+    yield "\n\n".join(log), "\n\n".join(transcripts), None, 5
+    try:
+        model = get_whisper_model(model_name)
+        log.append(f"Loaded Whisper model: {model_name}")
+    except Exception as e:
+        log.append(f"Failed to load model {model_name}: {e}")
+        yield "\n\n".join(log), "\n\n".join(transcripts), None, 100
+        return
+
+    total = len(paths)
+    idx = 0
+    for p in paths:
+        idx += 1
+        log.append(f"Processing file ({idx}/{total}): {p}")
+        yield "\n\n".join(log), "\n\n".join(transcripts), None, int(
+            5 + (idx - 1) * 80 / max(1, total)
+        )
+
+        wav = None
+        try:
+            wav = convert_to_wav_if_needed(p)
+            log.append(f"Converted to WAV: {wav}")
+        except Exception as e:
+            log.append(f"Conversion failed for {p}: {e}")
+            transcripts.append(
+                f"FILE: {os.path.basename(p)}\nERROR: Conversion failed: {e}"
+            )
+            yield "\n\n".join(log), "\n\n".join(transcripts), None, int(
+                5 + idx * 80 / max(1, total)
+            )
+            continue
+
+        try:
+            whisper_opts = {}
+            if isinstance(advanced_options, dict):
+                whisper_opts.update(advanced_options)
+
+            result = model.transcribe(wav, **whisper_opts)
+            text = result.get("text", "").strip()
+            log.append(f"Transcribed: {len(text)} chars")
+
+            if enable_memory:
+                text = memory_correct_text(text)
+            text = postprocess_transcript(text)
+            transcripts.append(
+                f"FILE: {os.path.basename(p)}\n{text}\n"
+            )
+
+            if enable_memory:
+                try:
+                    update_memory_with_transcript(text)
+                    log.append("Memory updated.")
+                except Exception:
+                    pass
+
+            yield "\n\n".join(log), "\n\n".join(transcripts), None, int(
+                10 + idx * 85 / max(1, total)
+            )
+        except Exception as e:
+            log.append(f"Transcription failed for {p}: {e}")
+            transcripts.append(
+                f"FILE: {os.path.basename(p)}\nERROR: Transcription failed: {e}"
+            )
+            yield "\n\n".join(log), "\n\n".join(transcripts), None, int(
+                10 + idx * 85 / max(1, total)
+            )
+            continue
+        finally:
+            try:
+                if wav and os.path.exists(wav):
+                    tmpdir = tempfile.gettempdir()
+                    if (
+                        os.path.commonpath([tmpdir, os.path.abspath(wav)])
+                        == tmpdir
+                        and not p.lower().endswith(".wav")
+                    ):
+                        os.unlink(wav)
+            except Exception:
+                pass
+
+    # final merge option
+    if merge_checkbox:
+        try:
+            merged_text = "\n\n".join(transcripts)
+            word_file_path = save_as_word(merged_text)
+            log.append(f"Merged transcript saved: {word_file_path}")
+        except Exception as e:
+            log.append(f"Failed to save merged file: {e}")
+            word_file_path = None
+
+    # final yield
+    yield "\n\n".join(log), "\n\n".join(transcripts), word_file_path, 100
+
+    # cleanup extracted dir
+    try:
+        if os.path.exists(temp_extract_dir):
+            shutil.rmtree(temp_extract_dir)
+            log.append("Cleaned temporary extraction dir.")
+    except Exception:
+        pass
+
+
+# ----------------------- Gradio wrapper (streaming) -----------------------
+def run_transcription_wrapper(
+    files,
+    model_name,
+    merge,
+    zip_file,
+    zip_password,
+    use_default_zip_pass,
+    default_zip_password,
+    enable_memory,
+    advanced_options_state,
+):
+    """
+    Gradio callback that streams results from transcribe_multiple.
+    This MUST be a generator (use yield) so Gradio receives 4 outputs,
+    not a single generator object.
+    """
+    try:
+        audio_input = files
+
+        # Normalize zip path
+        zip_path = None
+        if zip_file:
+            if isinstance(zip_file, (str, os.PathLike)):
+                zip_path = str(zip_file)
+            elif hasattr(zip_file, "name"):
+                zip_path = zip_file.name
+            elif isinstance(zip_file, dict) and zip_file.get("name"):
+                zip_path = zip_file["name"]
+
+        # Decide final zip password
+        if use_default_zip_pass and (not zip_password or zip_password.strip() == ""):
+            final_zip_password = default_zip_password
+        else:
+            final_zip_password = zip_password
+
+        adv = {}  # placeholder for future advanced options
+
+        # Stream results from the core generator
+        for logs, transcripts, word_path, percent in transcribe_multiple(
+            audio_input,
+            model_name,
+            adv,
+            merge_checkbox=merge,
+            zip_file=zip_path,
+            zip_password=final_zip_password,
+            enable_memory=enable_memory,
+        ):
+            yield logs, transcripts, word_path, percent
+
+    except Exception:
+        tb = traceback.format_exc()
+        logs = f"EXCEPTION in run_transcription_wrapper:\n{tb}"
+        transcripts = "ERROR: transcription did not start or failed unexpectedly."
+        yield logs, transcripts, None, 100
+
+
+print("DEBUG: building Gradio Blocks", flush=True)
+
+# ---------- Minimal, easy-to-use UI ----------
+with gr.Blocks(title="Whisper Transcriber") as demo:
+    gr.Markdown(
+        "## Whisper Transcriber\n"
+        "Upload audio files or a ZIP on the left and click **Transcribe**.\n"
+        "Transcript, progress, download, and logs appear on the right."
+    )
+
+    with gr.Row():
+        # LEFT: Inputs (small boxes, simple controls)
+        with gr.Column(scale=1):
+            gr.Markdown("### Input")
+
+            file_input = gr.File(
+                label="Audio files",
+                file_count="multiple",
+                type="filepath",
+                height=60,  # small box
+            )
+
+            zip_input = gr.File(
+                label="ZIP with audio (optional)",
+                file_count="single",
+                type="filepath",
+                height=60,  # small box
+            )
+
+            use_default_zip_pass = gr.Checkbox(
+                label="Use default ZIP password",
+                value=False,
+            )
+
+            default_zip_password = gr.Textbox(
+                label="Default ZIP password",
+                value="dietcoke1",  # you can change this
+                interactive=True,
+            )
+
+            zip_password = gr.Textbox(
+                label="ZIP password (override)",
+                placeholder="If empty, default password will be used",
+            )
+
+            model_select = gr.Dropdown(
+                choices=["small", "medium", "large", "base"],
+                value="small",
+                label="Whisper model",
+            )
+
+            merge_checkbox = gr.Checkbox(
+                label="Merge all transcripts into one .docx",
+                value=True,
+            )
+
+            memory_checkbox = gr.Checkbox(
+                label="Enable correction memory",
+                value=False,
+            )
+
+            submit = gr.Button("Transcribe", variant="primary")
+
+        # RIGHT: Outputs (Transcript → Progress → Download → Logs)
+        with gr.Column(scale=1):
+            gr.Markdown("### Output")
+
+            transcripts_out = gr.Textbox(
+                label="Transcript",
+                lines=18,
+                interactive=False,
+            )
+
+            progress_num = gr.Slider(
+                minimum=0,
+                maximum=100,
+                value=0,
+                step=1,
+                label="Progress (%)",
+                interactive=False,
+            )
+
+            download_file = gr.File(
+                label="Merged .docx (when available)"
+            )
+
+            logs = gr.Textbox(
+                label="Logs",
+                lines=10,
+                interactive=False,
+            )
+
+    submit.click(
+        fn=run_transcription_wrapper,
+        inputs=[
+            file_input,
+            model_select,
+            merge_checkbox,
+            zip_input,
+            zip_password,
+            use_default_zip_pass,
+            default_zip_password,
+            memory_checkbox,
+            gr.State({}),
+        ],
+        # The function yields: logs, transcripts, download_file, progress
+        outputs=[logs, transcripts_out, download_file, progress_num],
+    )
+
+# ---------- Launch ----------
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    print("DEBUG: launching Gradio on port", port, flush=True)
+    try:
+        demo.queue().launch(server_name="0.0.0.0", server_port=port)
+    except Exception as e:
+        print("FATAL: demo.launch failed:", e, flush=True)
+        traceback.print_exc()
+        raise
+ChatGPT said:
+
+Thought for 7s
+Nice, thanks — this is a solid, well-structured transcription app. I ran a careful code review and made a few targeted fixes and improvements you can drop into your file to make it more robust and reliable (especially around ffmpeg fallback conversion, ZIP password handling, temp-file cleanup, and a couple of small safety/usability items).
+
+Below I list the important issues I found, why they matter, and then provide the concrete patched code snippets you can replace in your file. I kept changes minimal and backward-compatible.
+
+Major issues & fixes (summary)
+ffmpeg conversion command was fragile
+You used -f <fmt> before -i which tells ffmpeg the input format is raw (s16le/pcm), and that often fails for containerized inputs. Better: call ffmpeg -i input -ar <sr> -ac <ch> out.wav (let ffmpeg auto-detect input). If you still want raw-format attempts, do them explicitly only when necessary. The patch changes the command so ffmpeg autodetects input and sets output sampling/channel only.
+
+Diagnostics/logging & cleanup
+If conversion fails we should remove partial files and write diagnostics safely. I preserved your diagnostics but improved removal and error messages.
+
+ZIP password handling with pyzipper
+zf.setpassword(...) simply sets a default password — it does not throw on set. Extraction will raise if password is wrong. I changed code to try extract and catch RuntimeError/RuntimeError/pyzipper.BadZipFile where appropriate and produce a clearer "Wrong password" message.
+
+Temporary WAV deletion logic
+The logic that deletes intermediate WAV relied on checking os.path.commonpath([tmpdir, os.path.abspath(wav)]) == tmpdir — that may raise if paths are on different drives. I hardened that check and wrapped in try/except.
+
+Model loading
+I kept your model cache but suggested an optional device override if you want to put it on CPU/GPU. (Small note: confirm whisper.load_model(name) is the right API for the whisper package/version you are using.)
+
+Minor UI suggestion
+Consider setting file_input's type="file" or keeping "filepath" with a fallback handler — your run_transcription_wrapper already copes with zip_file being file-like or path-like, so it's fine. (Left unchanged.)
+
+Replace these functions with the patched versions
+Paste these function definitions into your file to replace the originals for _ffmpeg_convert, convert_to_wav_if_needed, and the ZIP extraction block in transcribe_multiple. I preserved your variable names and diagnostics as much as possible.
+
+# ---------- improved ffmpeg convert ----------
+def _ffmpeg_convert(input_path, out_path, fmt, sr, ch):
+    """
+    Use ffmpeg to convert input_path -> out_path.
+    We let ffmpeg autodetect input format (so don't force -f except if fmt == 'raw').
+    If fmt is one of the raw formats (s16le/pcm_s16le/mulaw) we treat it as raw input;
+    otherwise we let ffmpeg autodetect and only set sample rate/channels on output.
+    """
+    try:
+        cmd = ["ffmpeg", "-hide_banner", "-loglevel", "error", "-y"]
+
+        # if fmt indicates raw input format, we need to tell ffmpeg about it
+        if fmt in ("s16le", "pcm_s16le", "mulaw"):
+            # raw PCM input requires -f and input format description before -i
+            cmd += ["-f", fmt, "-ar", str(sr), "-ac", str(ch), "-i", input_path]
+            # output will be WAV (ffmpeg determines container from out_path)
+            cmd += [out_path]
+        else:
+            # For most candidate attempts, let ffmpeg autodetect input format.
+            # Request output sample rate/channels explicitly.
+            cmd += ["-i", input_path, "-ar", str(sr), "-ac", str(ch), out_path]
+
+        proc = subprocess.run(cmd, capture_output=True, timeout=60, text=True)
+        stdout_stderr = proc.stdout + proc.stderr
+        if proc.returncode == 0 and os.path.exists(out_path) and os.path.getsize(out_path) > MIN_WAV_SIZE:
+            return True, stdout_stderr
+        else:
+            # remove partial output if exists
+            try:
+                if os.path.exists(out_path):
+                    os.unlink(out_path)
+            except Exception:
+                pass
+            return False, stdout_stderr
+    except Exception as e:
+        try:
+            if os.path.exists(out_path):
+                os.unlink(out_path)
+        except Exception:
+            pass
+        return False, str(e)
+
+
+def convert_to_wav_if_needed(input_path):
+    input_path = str(input_path)
+    lower = input_path.lower()
+    if lower.endswith(".wav"):
+        return input_path
+
+    auto_err = ""
+    tmp = None
+    try:
+        tmp = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+        tmp.close()
+        # let pydub autodetect format for most files
+        AudioSegment.from_file(input_path).export(tmp.name, format="wav")
+        if os.path.exists(tmp.name) and os.path.getsize(tmp.name) > MIN_WAV_SIZE:
+            return tmp.name
+        else:
+            # bad export, remove and fall through to ffmpeg tries
+            try:
+                os.unlink(tmp.name)
+            except Exception:
+                pass
+    except Exception:
+        auto_err = traceback.format_exc()
+        try:
+            if tmp and os.path.exists(tmp.name):
+                os.unlink(tmp.name)
+        except Exception:
+            pass
+
+    # ffmpeg fallback
+    diag_dir = tempfile.mkdtemp(prefix="dct_diag_")
+    diag_log = os.path.join(diag_dir, "conversion_diagnostics.txt")
+    diagnostics = []
+    for fmt, sr, ch in FFMPEG_CANDIDATES:
+        out_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+        out_wav.close()
+        success, debug = _ffmpeg_convert(input_path, out_wav.name, fmt, sr, ch)
+        diagnostics.append(f"TRY fmt={fmt} sr={sr} ch={ch} success={success}\n{debug}\n")
+        if success:
+            try:
+                with open(diag_log, "w", encoding="utf-8") as fh:
+                    fh.write("pydub auto error:\n")
+                    fh.write(auto_err + "\n\n")
+                    fh.write("Successful ffmpeg candidate:\n")
+                    fh.write(f"fmt={fmt} sr={sr} ch={ch}\n\n")
+                    fh.write("Diagnostics:\n")
+                    fh.write("\n".join(diagnostics))
+            except Exception:
+                pass
+            return out_wav.name
+        else:
+            try:
+                if os.path.exists(out_wav.name):
+                    os.unlink(out_wav.name)
+            except Exception:
+                pass
+
+    # final diagnostics: ffprobe + file head
+    try:
+        fp = subprocess.run(
+            ["ffprobe", "-v", "error", "-show_format", "-show_streams", input_path],
+            capture_output=True,
+            text=True,
+            timeout=10,
+        )
+        diagnostics.append("FFPROBE:\n" + (fp.stdout.strip() or fp.stderr.strip()))
+    except Exception as e:
+        diagnostics.append("ffprobe failed: " + str(e))
+    try:
+        with open(input_path, "rb") as fh:
+            head = fh.read(512)
+            diagnostics.append("HEX PREVIEW:\n" + head.hex())
+    except Exception as e:
+        diagnostics.append("could not read head: " + str(e))
+
+    try:
+        with open(diag_log, "w", encoding="utf-8") as fh:
+            fh.write("pydub auto error:\n")
+            fh.write(auto_err + "\n\n")
+            fh.write("Full diagnostics:\n\n")
+            fh.write("\n\n".join(diagnostics))
+    except Exception as e:
+        # keep original behavior: surface a readable exception
+        raise Exception(f"Conversion failed; diagnostics write error: {e}")
+
+    raise Exception(f"Could not convert file to WAV. Diagnostics saved to: {diag_log}")
+Replace the ZIP extraction block inside transcribe_multiple with this improved version
+Find this earlier in transcribe_multiple:
+
+    if zip_file:
+        log.append(f"Processing zip: {zip_file}")
+        yield "\n\n".join(log), "\n\n".join(transcripts), None, 2
+        try:
+            os.makedirs(temp_extract_dir, exist_ok=True)
+            with pyzipper.ZipFile(zip_file, "r") as zf:
+                if zip_password:
+                    try:
+                        zf.setpassword(zip_password.encode())
+                    except Exception:
+                        log.append("Incorrect zip password")
+                        yield "\n\n".join(log), "\n\n".join(transcripts), None, 100
+                        return
+                exts = [...]
+                ...
+Replace the whole if zip_file: block with this:
+
+    if zip_file:
+        log.append(f"Processing zip: {zip_file}")
+        yield "\n\n".join(log), "\n\n".join(transcripts), None, 2
+        try:
+            os.makedirs(temp_extract_dir, exist_ok=True)
+            with pyzipper.ZipFile(zip_file, "r") as zf:
+                # set password if provided (pyzipper accepts bytes)
+                if zip_password:
+                    try:
+                        zf.setpassword(zip_password.encode())
+                    except Exception:
+                        # setpassword typically doesn't fail, but just in case
+                        log.append("Failed to set zip password (unexpected).")
+                exts = [
+                    ".mp3",
+                    ".wav",
+                    ".aac",
+                    ".flac",
+                    ".ogg",
+                    ".m4a",
+                    ".dat",
+                    ".dct",
+                ]
+                count = 0
+                for info in zf.infolist():
+                    if info.is_dir():
+                        continue
+                    _, ext = os.path.splitext(info.filename)
+                    if ext.lower() in exts:
+                        try:
+                            # attempt to extract each file; if wrong password, extraction will raise
+                            zf.extract(info, path=temp_extract_dir)
+                        except RuntimeError as e:
+                            # pyzipper may raise RuntimeError on bad password
+                            log.append(f"Password required or incorrect for {info.filename}: {e}")
+                            continue
+                        except pyzipper.BadZipFile:
+                            log.append(f"Bad zip entry: {info.filename}")
+                            continue
+                        except Exception as e:
+                            log.append(f"Error extracting {info.filename}: {e}")
+                            continue
+                        p = os.path.normpath(os.path.join(temp_extract_dir, info.filename))
+                        if os.path.exists(p):
+                            extracted_audio_paths.append(p)
+                            count += 1
+                            log.append(f"Extracted: {info.filename}")
+                if count == 0:
+                    log.append("No supported audio in zip.")
+                    try:
+                        shutil.rmtree(temp_extract_dir)
+                    except Exception:
+                        pass
+                    yield "\n\n".join(log), "\n\n".join(transcripts), None, 100
+                    return
+        except pyzipper.BadZipFile:
+            log.append("Invalid zip file.")
+            try:
+                shutil.rmtree(temp_extract_dir)
+            except Exception:
+                pass
+            yield "\n\n".join(log), "\n\n".join(transcripts), None, 100
+            return
+        except Exception as e:
+            log.append(f"Zip processing error: {e}")
+            try:
+                shutil.rmtree(temp_extract_dir)
+            except Exception:
+                pass
+            yield "\n\n".join(log), "\n\n".join(transcripts), None, 100
+            return
+Small hardening for temporary WAV deletion (replace your existing finally block)
+Replace the finally that deletes intermediate WAVs with this safer variant:
+
+            finally:
+                try:
+                    if wav and os.path.exists(wav):
+                        tmpdir = tempfile.gettempdir()
+                        try:
+                            # Do not remove original .wav files that came from user (only remove conversion outputs)
+                            common = os.path.commonpath([os.path.abspath(tmpdir), os.path.abspath(wav)])
+                            if common == os.path.abspath(tmpdir) and not p.lower().endswith(".wav"):
+                                os.unlink(wav)
+                        except Exception:
+                            # fallback: only remove if it's in tempdir substring
+                            try:
+                                if tmpdir in os.path.abspath(wav) and not p.lower().endswith(".wav"):
+                                    os.unlink(wav)
+                            except Exception:
+                                pass
+                except Exception:
+                    pass
+Optional suggestions (non-blocking)
+Consider making MIN_WAV_SIZE a bit larger (e.g., 1024 bytes) — 200 bytes is tiny and can be a partial/invalid file.
+
+Consider exposing a device option for the Whisper model (e.g. cpu vs cuda) and pass that into whisper.load_model.
+
+default_zip_password is in your UI — don't keep real secrets there; make default generic or blank.
+
+If you want progress more precise, you could measure durations of actual model transcribe calls and update percent accordingly.
+
+If you run on Hugging Face Spaces, ensure the whisper package you installed matches your API expectations (some forks differ).
+
+Why these changes help (TL;DR)
+Letting ffmpeg autodetect input prevents many conversion failures on real-world audio files (mp3, m4a, flac, etc.).
+
+Proper diagnostic capture + safer temp-file cleanup prevents stuck files and makes debugging easier.
+
+More robust zip/password extraction reduces silent failures and makes user feedback clearer.
+
+If you want, I can:
+
+produce a full patched app.py with the above replacements merged (ready to paste), or
+
+convert model loading to allow mobile/CPU-only fallback and add a small "device" dropdown in the UI, or
+
+update the UI to add a "select which extracted files to transcribe" step (you had requested that in earlier project notes).
+
+Tell me which of those you want and I’ll give the full code in one paste.
+
+You said:
+full code with option to memories old words
+ChatGPT said:
+
+Thought for 5s
+Nice — below is a full, ready-to-paste app.py that:
+
+Includes the improved ffmpeg fallback conversion and safer temp cleanup from my earlier suggestions.
+
+Improves ZIP extraction & password handling.
+
+Adds Memory management UI and functions so you can:
+
+Enable/disable the correction memory (existing checkbox).
+
+Import an existing memory file (JSON or newline word list).
+
+Add a single word or phrase to memory manually.
+
+Clear the memory.
+
+View current memory (words & phrases counts).
+
+Keeps the original transcription flow and UI layout; I only extended the left-side controls to include the memory operations.
+
+Paste this into your repo (replace your current app.py). I preserved your original structure and most variable names.
+
 # app.py
 # Whisper transcription app - HYBRID conversion (pydub + small ffmpeg fallback)
 # Clean, single-version file for Hugging Face Spaces.
@@ -934,3 +2047,31 @@ if __name__ == "__main__":
         print("FATAL: demo.launch failed:", e, flush=True)
         traceback.print_exc()
         raise
+Notes & usage tips
+Importable memory formats:
+
+JSON with { "words": {"word": count, ...}, "phrases": {"some phrase": count, ...} }
+
+Plain text with each line a word (or word,count), or longer lines will be treated as phrases.
+
+Enable correction memory should be checked to apply memory-based corrections during transcription.
+
+View Memory shows the top 50 words/phrases (truncated for UI).
+
+Clear Memory removes all stored words & phrases (and overwrites memory.json).
+
+If you'd like I can:
+
+produce a version that also shows a small popup listing extracted files from a ZIP and lets the user choose which to transcribe (you mentioned that previously), or
+
+add a "device" dropdown (cpu/cuda) and pass to whisper.load_model, or
+
+create a minimal tests script that runs conversion on a few sample audio files to validate ffmpeg fallbacks.
+
+Which follow-up would you like?
+
+
+
+
+No file chosenNo file chosen
+ChatGPT can make mistakes. Check important info. See Cookie Preferences.