feat: use ollama to cleanup context window

6 files changed, 410 insertions, 39 deletions

diff --git a/README.md b/README.md
index 9216fd0..5c7803e 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,25 @@
 # auto-live-tl
-A basic LOCAL translation backend that listens to an audio sink via PCM and runs translation via faster-whisper
+A basic LOCAL translation backend that listens to an audio sink via PCM and runs translation via faster-whisper. Also supports the option to use `qwen2.5-7b-instruct` to format/clean-up subtitles based on sliding window context.
 
 Translations and trascriptions are transformers based, inaccuracies and hallucinations will occur.
 
+
+# Setup
 > It's highly recommended that you run this with a GPU, running with CPU is possible but inference will be very slow outside of using tiny models (which compromise accuracy)
 >
 > For this, you will need to install a Nvidia CUDA 12 toolkit. I am running with [CUDA Toolkit 12.9](https://developer.nvidia.com/cuda-12-9-0-download-archive)
 
+```
+uv sync
+uv run server.py
+```
+A GUI is available for configuration
+
 `server.py` serves a backend for translating incoming audio data. It expects some other client to hit the `/events` endpoint to fetch the translated data.
 
+
+# Clients:
+
 `youtube-subtitle.user.js` is one such example client that can fetch data from this endpoint and render it beneath a YouTube video. You can install it as a userscript.
 
 <img width="1210" height="109" alt="image" src="https://github.com/user-attachments/assets/2bffde45-bc61-4d63-b779-b7a8cd183bc0" />
diff --git a/config.py b/config.py
new file mode 100644
index 0000000..1101085
--- /dev/null
+++ b/config.py
@@ -0,0 +1,24 @@
+_SYSTEM_PROMPT: str = (
+    "You are a live-stream subtitle deduplicator and sentence completer.\n"
+    "The speech-to-text engine uses a ROLLING AUDIO WINDOW, so every new "
+    "raw input re-transcribes the recent past verbatim. Most of the raw "
+    "input is old text already shown to the viewer.\n\n"
+    "ALREADY SHOWN lists every subtitle line already displayed.\n\n"
+    "YOUR JOB:\n"
+    "Extract only the genuinely NEW spoken content from the raw input, "
+    "while ensuring the output forms clean, complete, natural sentences.\n\n"
+    "STRICT RULES:\n"
+    "  1. NEVER repeat text that is already fully covered by ALREADY SHOWN.\n"
+    "  2. Prefer returning COMPLETE SENTENCES instead of cut-off fragments.\n"
+    "     If the new content starts mid-sentence, use the rolling context "
+    "     from the raw input to complete the full sentence naturally.\n"
+    "  3. Do NOT paraphrase, summarize, or invent meaning — preserve the "
+    "     speaker's original wording as closely as possible.\n"
+    "  4. You may use overlapping words from the raw input only when needed "
+    "     to reconstruct a full readable sentence, but avoid unnecessary repetition.\n"
+    "  5. Fix punctuation, capitalization, and obvious transcript artifacts "
+    "     (like duplicated partial words) for readability.\n"
+    "  6. If the entire raw input is already covered by ALREADY SHOWN, "
+    "     output an empty string and nothing else.\n"
+    "  7. Output ONLY the final subtitle text. No labels, no explanations."
+)
diff --git a/gui.py b/gui.py
index aece888..7425567 100644
--- a/gui.py
+++ b/gui.py
@@ -20,16 +20,21 @@ def select_settings(
         return settings.get(key, default_settings.get(key, fallback))
 
     root = tk.Tk()
-    root.title("Audio & Whisper Settings")
+    root.title("Settings")
     root.resizable(False, False)
 
-    frame = ttk.Frame(root, padding=10)
-    frame.pack(fill="both", expand=True)
-    frame.columnconfigure(1, weight=1)
+    notebook = ttk.Notebook(root)
+    notebook.pack(fill="both", expand=True, padx=10, pady=(10, 0))
 
-    def add_row(row: int, label_text: str, widget: tk.Widget) -> None:
-        label = ttk.Label(frame, text=label_text)
-        label.grid(row=row, column=0, sticky="w", pady=4)
+    # ------------------------------------------------------------------ #
+    # Tab 1 – Whisper                                                      #
+    # ------------------------------------------------------------------ #
+    whisper_tab = ttk.Frame(notebook, padding=10)
+    whisper_tab.columnconfigure(1, weight=1)
+    notebook.add(whisper_tab, text="Whisper")
+
+    def add_row(parent: ttk.Frame, row: int, label_text: str, widget: tk.Widget) -> None:
+        ttk.Label(parent, text=label_text).grid(row=row, column=0, sticky="w", pady=4, padx=(0, 12))
         widget.grid(row=row, column=1, sticky="ew", pady=4)
 
     device_options = [
@@ -37,70 +42,92 @@ def select_settings(
         for idx, dev in input_devices
     ]
     device_names = [dev["name"] for _idx, dev in input_devices]
-    device_combo = ttk.Combobox(frame, values=device_options, state="readonly", width=60)
-
+    device_combo = ttk.Combobox(whisper_tab, values=device_options, state="readonly", width=60)
     default_device_name = get_value("audio_device_name", "")
     if default_device_name in device_names:
         device_combo.current(device_names.index(default_device_name))
     else:
         device_combo.current(0)
-    add_row(0, "Audio input device:", device_combo)
+    add_row(whisper_tab, 0, "Audio input device:", device_combo)
 
     model_var = tk.StringVar(value=get_value("model_name", "medium"))
-    model_combo = ttk.Combobox(frame, values=list(model_choices), textvariable=model_var)
+    model_combo = ttk.Combobox(whisper_tab, values=list(model_choices), textvariable=model_var)
     model_combo.set(model_var.get())
-    add_row(1, "Model:", model_combo)
+    add_row(whisper_tab, 1, "Model:", model_combo)
 
     device_type_var = tk.StringVar(value=get_value("device", "cpu"))
     device_type_combo = ttk.Combobox(
-        frame, values=list(device_choices), textvariable=device_type_var, state="readonly"
+        whisper_tab, values=list(device_choices), textvariable=device_type_var, state="readonly"
     )
     device_type_combo.set(device_type_var.get())
-    add_row(2, "Compute device:", device_type_combo)
+    add_row(whisper_tab, 2, "Compute device:", device_type_combo)
 
     compute_type_var = tk.StringVar(value=get_value("compute_type", "int8"))
-    compute_type_combo = ttk.Combobox(
-        frame, values=list(compute_choices), textvariable=compute_type_var
-    )
+    compute_type_combo = ttk.Combobox(whisper_tab, values=list(compute_choices), textvariable=compute_type_var)
     compute_type_combo.set(compute_type_var.get())
-    add_row(3, "Compute type:", compute_type_combo)
+    add_row(whisper_tab, 3, "Compute type:", compute_type_combo)
 
     task_var = tk.StringVar(value=get_value("task", "translate"))
-    task_combo = ttk.Combobox(frame, values=list(task_choices), textvariable=task_var, state="readonly")
+    task_combo = ttk.Combobox(whisper_tab, values=list(task_choices), textvariable=task_var, state="readonly")
     task_combo.set(task_var.get())
-    add_row(4, "Task:", task_combo)
+    add_row(whisper_tab, 4, "Task:", task_combo)
 
     beam_size_var = tk.StringVar(value=str(get_value("beam_size", 3)))
-    beam_entry = ttk.Entry(frame, textvariable=beam_size_var, width=10)
-    add_row(5, "Beam size:", beam_entry)
+    add_row(whisper_tab, 5, "Beam size:", ttk.Entry(whisper_tab, textvariable=beam_size_var, width=10))
 
     language_var = tk.StringVar(value=get_value("language", ""))
-    language_entry = ttk.Entry(frame, textvariable=language_var)
-    add_row(6, "Language (optional):", language_entry)
+    add_row(whisper_tab, 6, "Language (optional):", ttk.Entry(whisper_tab, textvariable=language_var))
 
     context_seconds_var = tk.StringVar(value=str(get_value("context_seconds", 10)))
-    context_entry = ttk.Entry(frame, textvariable=context_seconds_var, width=10)
-    add_row(7, "Context seconds:", context_entry)
+    add_row(whisper_tab, 7, "Context seconds:", ttk.Entry(whisper_tab, textvariable=context_seconds_var, width=10))
 
     update_interval_var = tk.StringVar(value=str(get_value("update_interval_seconds", 2)))
-    update_interval_entry = ttk.Entry(frame, textvariable=update_interval_var, width=10)
-    add_row(8, "Update interval (s):", update_interval_entry)
+    add_row(whisper_tab, 8, "Update interval (s):", ttk.Entry(whisper_tab, textvariable=update_interval_var, width=10))
+
+    # ------------------------------------------------------------------ #
+    # Tab 2 – Ollama                                                       #
+    # ------------------------------------------------------------------ #
+    ollama_tab = ttk.Frame(notebook, padding=10)
+    ollama_tab.columnconfigure(1, weight=1)
+    notebook.add(ollama_tab, text="Ollama")
+
+    use_ollama_cleanup_var = tk.BooleanVar(value=get_value("use_ollama_cleanup", True))
+    add_row(ollama_tab, 0, "LLM subtitle cleanup:", ttk.Checkbutton(ollama_tab, variable=use_ollama_cleanup_var))
+
+    ollama_device_var = tk.StringVar(value=get_value("ollama_device", "CPU"))
+    ollama_device_combo = ttk.Combobox(
+        ollama_tab, values=["CPU", "GPU"], textvariable=ollama_device_var, state="readonly", width=10
+    )
+    ollama_device_combo.set(ollama_device_var.get())
+    add_row(ollama_tab, 1, "Ollama compute:", ollama_device_combo)
 
-    button_frame = ttk.Frame(root, padding=(10, 0, 10, 10))
+    ollama_context_var = tk.StringVar(value=str(get_value("ollama_context_window", 6)))
+    add_row(ollama_tab, 2, "Context window (segments):", ttk.Entry(ollama_tab, textvariable=ollama_context_var, width=10))
+
+    ollama_batch_var = tk.StringVar(value=str(get_value("ollama_raw_batch_size", 3)))
+    add_row(ollama_tab, 3, "Batch size (lines per LLM call):", ttk.Entry(ollama_tab, textvariable=ollama_batch_var, width=10))
+
+    # ------------------------------------------------------------------ #
+    # Buttons                                                              #
+    # ------------------------------------------------------------------ #
+    button_frame = ttk.Frame(root, padding=(10, 6, 10, 10))
     button_frame.pack(fill="x")
 
     selected_settings: Dict[str, Any] = {}
 
     def on_ok() -> None:
         nonlocal selected_settings
+
         selection = device_combo.current()
         if selection < 0:
             messagebox.showwarning("Select a device", "Please select an audio input device.")
             return
+
         model_name = model_var.get().strip()
         if not model_name:
             messagebox.showwarning("Model required", "Please select or enter a model name.")
             return
+
         try:
             beam_size = int(beam_size_var.get().strip())
             if beam_size <= 0:
@@ -108,6 +135,7 @@ def select_settings(
         except ValueError:
             messagebox.showwarning("Invalid beam size", "Beam size must be a positive integer.")
             return
+
         try:
             context_seconds = float(context_seconds_var.get().strip())
             if context_seconds <= 0:
@@ -115,15 +143,31 @@ def select_settings(
         except ValueError:
             messagebox.showwarning("Invalid context seconds", "Context seconds must be a positive number.")
             return
+
         try:
             update_interval_seconds = float(update_interval_var.get().strip())
             if update_interval_seconds <= 0:
                 raise ValueError
         except ValueError:
-            messagebox.showwarning(
-                "Invalid update interval", "Update interval must be a positive number."
-            )
+            messagebox.showwarning("Invalid update interval", "Update interval must be a positive number.")
+            return
+
+        try:
+            ollama_context_window = int(ollama_context_var.get().strip())
+            if ollama_context_window <= 0:
+                raise ValueError
+        except ValueError:
+            messagebox.showwarning("Invalid context window", "Context window must be a positive integer.")
             return
+
+        try:
+            ollama_raw_batch_size = int(ollama_batch_var.get().strip())
+            if ollama_raw_batch_size <= 0:
+                raise ValueError
+        except ValueError:
+            messagebox.showwarning("Invalid batch size", "Batch size must be a positive integer.")
+            return
+
         selected_settings = {
             "audio_device_name": device_names[selection],
             "model_name": model_name,
@@ -134,6 +178,10 @@ def select_settings(
             "language": language_var.get().strip(),
             "context_seconds": context_seconds,
             "update_interval_seconds": update_interval_seconds,
+            "use_ollama_cleanup": use_ollama_cleanup_var.get(),
+            "ollama_device": ollama_device_var.get(),
+            "ollama_context_window": ollama_context_window,
+            "ollama_raw_batch_size": ollama_raw_batch_size,
         }
         root.quit()
 
@@ -185,4 +233,4 @@ def prompt_input_sample_rate(device_index: int, common_rates: Iterable[int] | No
                     parent=root,
                 )
     finally:
-        root.destroy()
\ No newline at end of file
+        root.destroy()
diff --git a/pyproject.toml b/pyproject.toml
index 37c0cd5..0f28a93 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,5 +8,6 @@ dependencies = [
     "faster-whisper>=1.2.1",
     "flask>=3.1.3",
     "flask-cors>=6.0.2",
+    "ollama>=0.6.1",
     "sounddevice>=0.5.5",
 ]
diff --git a/server.py b/server.py
index 35916b7..6831495 100644
--- a/server.py
+++ b/server.py
@@ -3,14 +3,20 @@ import threading
 import json
 import queue
 import os
-from typing import Any, Dict, Optional, Set, List, Iterator
+from collections import Counter, deque
+import re
+from typing import Any, Deque, Dict, Optional, Set, List, Iterator
 from flask import Flask
 from flask_cors import CORS
+import ollama as _ollama
+from ollama import chat
+from ollama import ChatResponse
 import numpy as np
 import sounddevice as sd
 from faster_whisper import WhisperModel
 from gui import select_settings, prompt_input_sample_rate
 from routes import register_routes
+from config import _SYSTEM_PROMPT
 
 TARGET_SAMPLE_RATE: int = 16000
 CAPTURE_SAMPLE_RATE: int = 0
@@ -20,6 +26,12 @@ PROCESS_INTERVAL_SECONDS: float = 2
 SSE_EVENT_SUBTITLE: str = "subtitle"
 SSE_KEEPALIVE_SECONDS: int = 15
 
+USE_OLLAMA_CLEANUP: bool = True
+OLLAMA_MODEL: str = "qwen2.5:7b-instruct"
+OLLAMA_CONTEXT_WINDOW: int = 6  # number of recent cleaned segments kept as context
+OLLAMA_OPTIONS: Dict[str, Any] = {"num_gpu": 1}
+RAW_BATCH_SIZE: int = 2  # accumulate this many raw Whisper lines before calling the LLM
+
 SETTINGS_PATH: str = os.path.join(os.path.dirname(__file__), "settings.json")
 
 DEFAULT_SETTINGS: Dict[str, Any] = {
@@ -32,6 +44,10 @@ DEFAULT_SETTINGS: Dict[str, Any] = {
     "language": "",
     "context_seconds": 10,
     "update_interval_seconds": 2,
+    "use_ollama_cleanup": True,
+    "ollama_device": "GPU",
+    "ollama_context_window": 5,
+    "ollama_raw_batch_size": 2,
 }
 
 MODEL_CHOICES: List[str] = ["tiny", "base", "small", "medium", "large-v2", "large-v3", "distil-large-v3"]
@@ -54,6 +70,12 @@ SERVER_PORT: int = 5000
 app: Flask = Flask(__name__)
 CORS(app)
 
+# OLLAMA stuff
+llm_input_queue: queue.Queue = queue.Queue(maxsize=1)
+subtitle_context: Deque[str] = deque(maxlen=OLLAMA_CONTEXT_WINDOW) # sliding window context
+subtitle_context_lock: threading.Lock = threading.Lock()
+_raw_batch: List[str] = []
+_raw_batch_lock: threading.Lock = threading.Lock()
 
 def resample_audio(audio_np: np.ndarray, src_rate: int, dst_rate: int) -> np.ndarray:
     if src_rate == dst_rate:
@@ -90,18 +112,163 @@ def save_settings(settings: Dict[str, Any]) -> None:
     except OSError as exc:
         print(f"Failed to save settings: {exc}")
 
+def cleanup_subtitle_with_ollama(raw_text: str, context: List[str]) -> Optional[str]:
+    if context:
+        context_block = "\n".join(f"- {seg}" for seg in context)
+    else:
+        context_block = "(none yet)"
+
+    user_message = (
+        f"ALREADY SHOWN:\n{context_block}\n\n"
+        "RAW INPUT (multiple consecutive transcriptions of the same rolling window — "
+        f"deduplicate and extract only the genuinely new spoken content as one subtitle):\n{raw_text}"
+    )
+
+    try:
+        response: ChatResponse = chat(
+            model=OLLAMA_MODEL,
+            messages=[
+                {"role": "system", "content": _SYSTEM_PROMPT},
+                {"role": "user", "content": user_message},
+            ],
+            options=OLLAMA_OPTIONS,
+        )
+        return response.message.content.strip()
+    except Exception as exc:
+        print(f"⚠️  OLLAMA cleanup error: {exc}")
+        return None
+
+
+def ensure_ollama_ready() -> None:
+    try:
+        local = _ollama.list()
+    except Exception as exc:
+        raise RuntimeError(
+            f"Cannot reach Ollama — is the server running?  ({exc})"
+        ) from exc
+    model_names: List[str] = [m.model for m in local.models]
+    if not any(name.startswith(OLLAMA_MODEL) for name in model_names):
+        print(f"   '{OLLAMA_MODEL}' not found locally — pulling (this may take a while) ...")
+        try:
+            _ollama.pull(OLLAMA_MODEL)
+            print("   Pull complete.")
+        except Exception as exc:
+            raise RuntimeError(f"Failed to pull model '{OLLAMA_MODEL}': {exc}") from exc
+    else:
+        print(f"   Model found locally.")
+    print("   Warming up model, almost done ...")
+    try:
+        chat(
+            model=OLLAMA_MODEL,
+            messages=[{"role": "user", "content": "Ready?"}],
+            options=OLLAMA_OPTIONS,
+        )
+        print("   ✅ Ollama is ready.")
+    except Exception as exc:
+        raise RuntimeError(f"Ollama warm-up failed: {exc}") from exc
+
+_LLM_EMPTY_SENTINELS: frozenset = frozenset({
+    "empty string", "empty", "(empty)", "[empty]",
+    "(empty string)", "[empty string]", "(none)", "none", "n/a",
+})
+
+
+def normalize_llm_output(text: str) -> str:
+    if text.strip().lower().rstrip(".") in _LLM_EMPTY_SENTINELS:
+        return ""
+    return text
+
+
+def is_hallucination(text: str) -> bool:
+    words = text.split()
+    if not words:
+        return False
+    max_expected = int(BUFFER_SECONDS * 4.5)
+    if len(words) > max_expected:
+        print(f"🔴 Hallucination (too long: {len(words)} words > {max_expected}): {text[:60]!r}")
+        return True
+    clean = [re.sub(r"[^\w']+", "", w).lower() for w in words]
+    clean = [w for w in clean if w]
+    for n in (2, 3):
+        if len(clean) < n * 3:
+            continue
+        ngrams = [" ".join(clean[i : i + n]) for i in range(len(clean) - n + 1)]
+        top, count = Counter(ngrams).most_common(1)[0]
+        if count >= 3:
+            print(f"🔴 Hallucination (\'{top}\' x{count}): {text[:60]!r}")
+            return True
+    top, count = Counter(clean).most_common(1)[0]
+    if count >= 4 and count / len(clean) > 0.40:
+        print(f"🔴 Hallucination (\'{top}\' x{count}, {count/len(clean):.0%}): {text[:60]!r}")
+        return True
+    return False
+
+
+def llm_processing_loop() -> None:
+    print(f"LLM cleanup thread started (model={OLLAMA_MODEL})")
+    while True:
+        try:
+            raw_text: str = llm_input_queue.get(timeout=1)
+        except queue.Empty:
+            continue
+
+        with subtitle_context_lock:
+            context = list(subtitle_context)
+
+        cleaned: Optional[str] = cleanup_subtitle_with_ollama(raw_text, context)
+
+        if cleaned is None:
+            cleaned = raw_text
+        else:
+            cleaned = normalize_llm_output(cleaned)
+
+        if cleaned:
+            with subtitle_context_lock:
+                subtitle_context.append(cleaned)
+            print(f"🔵 (cleaned) {cleaned}")
+            broadcast_subtitle(cleaned)
+        else:
+            print("🟡 (LLM: no new content)")
+
 
 def run_whisper(audio_np: np.ndarray) -> str:
     transcribe_kwargs: Dict[str, Any] = {"task": WHISPER_TASK, "beam_size": WHISPER_BEAM_SIZE}
     if WHISPER_LANGUAGE:
         transcribe_kwargs["language"] = WHISPER_LANGUAGE
-    # model is expected to be initialized in main()
     assert model is not None, "Whisper model is not initialized"
     segments, _info = model.transcribe(audio_np, **transcribe_kwargs)
     text = " ".join(seg.text for seg in segments).strip()
-    if text:
-        print("🟢", text)
+    if not text:
+        return text
+
+    print(f"🟢 (raw) {text}")
+
+    if is_hallucination(text):
+        return text
+
+    if USE_OLLAMA_CLEANUP:
+        with _raw_batch_lock:
+            _raw_batch.append(text)
+            if len(_raw_batch) >= RAW_BATCH_SIZE:
+                batch_text = "\n".join(_raw_batch)
+                _raw_batch.clear()
+            else:
+                batch_text = None
+        if batch_text is not None:
+            try:
+                llm_input_queue.put_nowait(batch_text)
+            except queue.Full:
+                try:
+                    llm_input_queue.get_nowait()
+                except queue.Empty:
+                    pass
+                try:
+                    llm_input_queue.put_nowait(batch_text)
+                except queue.Full:
+                    pass
+    else:
         broadcast_subtitle(text)
+
     return text
 
 
@@ -225,7 +392,8 @@ def select_input_sample_rate(device_index: int, preferred_rate: int) -> int:
 
 def main() -> None:
     global CAPTURE_SAMPLE_RATE, MAX_SAMPLES, model, WHISPER_TASK, WHISPER_BEAM_SIZE, WHISPER_LANGUAGE
-    global BUFFER_SECONDS, PROCESS_INTERVAL_SECONDS
+    global BUFFER_SECONDS, PROCESS_INTERVAL_SECONDS, USE_OLLAMA_CLEANUP
+    global OLLAMA_CONTEXT_WINDOW, RAW_BATCH_SIZE, subtitle_context
     start_subtitle_server()
 
     settings: Dict[str, Any] = load_settings()
@@ -242,6 +410,16 @@ def main() -> None:
     )
     save_settings(settings)
 
+    USE_OLLAMA_CLEANUP = bool(settings.get("use_ollama_cleanup", True))
+    OLLAMA_OPTIONS["num_gpu"] = 0 if settings.get("ollama_device", "CPU").upper() == "CPU" else 1
+    OLLAMA_CONTEXT_WINDOW = int(settings.get("ollama_context_window", 6))
+    subtitle_context = deque(maxlen=OLLAMA_CONTEXT_WINDOW)
+    RAW_BATCH_SIZE = int(settings.get("ollama_raw_batch_size", 3))
+    if USE_OLLAMA_CLEANUP:
+        ensure_ollama_ready()
+        llm_thread = threading.Thread(target=llm_processing_loop, daemon=True)
+        llm_thread.start()
+
     device_name: str = settings.get("audio_device_name", "")
     matched_index: Optional[int] = None
     for idx, dev in enumerate(devices):
@@ -277,6 +455,8 @@ def main() -> None:
     print(f"Model: {model_name} | task={WHISPER_TASK} | beam_size={WHISPER_BEAM_SIZE}")
     print(f"Compute: device={whisper_device} | compute_type={compute_type}")
     print(f"Capture sample rate: {CAPTURE_SAMPLE_RATE} Hz (resampling to {TARGET_SAMPLE_RATE} Hz)")
+    print(f"Ollama cleanup: {'enabled' if USE_OLLAMA_CLEANUP else 'disabled'} (model={OLLAMA_MODEL})")
+
     processing_thread = threading.Thread(target=processing_loop, daemon=True)
     processing_thread.start()
     with sd.InputStream(
diff --git a/uv.lock b/uv.lock
index 37d8686..b882fe0 100644
--- a/uv.lock
+++ b/uv.lock
@@ -12,6 +12,15 @@ wheels = [
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 version = "4.13.0"
 source = { registry = "https://pypi.org/simple" }
@@ -31,6 +40,7 @@ dependencies = [
     { name = "faster-whisper" },
     { name = "flask" },
     { name = "flask-cors" },
+    { name = "ollama" },
     { name = "sounddevice" },
 ]
 
@@ -39,6 +49,7 @@ requires-dist = [
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     { name = "flask", specifier = ">=3.1.3" },
     { name = "flask-cors", specifier = ">=6.0.2" },
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@@ -501,6 +512,19 @@ wheels = [
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 version = "1.24.4"
 source = { registry = "https://pypi.org/simple" }
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