Initial commit: LocalAI-backed ScrAIbe with summarization

Dockerfile

@@ -1,44 +1,43 @@
-#pytorch Image
-FROM pytorch/pytorch:2.3.1-cuda12.1-cudnn8-runtime
+# Lightweight Python base image (no GPU/PyTorch needed)
+FROM python:3.11-slim
 
 # Labels
-
 LABEL maintainer="Jacob Schmieder"
 LABEL email="Jacob.Schmieder@dbfz.de"
 LABEL version="0.1.1.dev"
-LABEL description="Scraibe is a tool for automatic speech recognition and speaker diarization. \
-                    It is based on the Hugging Face Transformers library and the Pyannote library. \
-                    It is designed to be used with the Whisper model, a lightweight model for automatic \
-                    speech recognition and speaker diarization."
+LABEL description="Scraibe: LocalAI-backed transcription and diarization client with summarization. \
+                    Sends audio to a LocalAI server running vibevoice.cpp and uses a second LLM for summarization."
 LABEL url="https://github.com/JSchmie/ScrAIbe"
 
-# Install dependencies
-WORKDIR /app
-#Enviorment dependencies
-ENV TRANSFORMERS_CACHE=/app/models
-ENV HF_HOME=/app/models
-ENV AUTOT_CACHE=/app/models
-ENV PYANNOTE_CACHE=/app/models/pyannote
-#Copy all necessary files 
-COPY requirements.txt /app/requirements.txt
-COPY README.md /app/README.md
-COPY scraibe /app/scraibe
-
-#Installing all necessary dependencies and running the application with a personalised Hugging-Face-Token
-RUN apt update -y && apt upgrade -y && \
-    apt install -y libsm6 libxrender1 libfontconfig1 && \
+# Install system dependencies (ffmpeg required)
+RUN apt update -y && \
+    apt install -y --no-install-recommends ffmpeg && \
     apt clean && \
     rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
 
-RUN conda update --all && \
-    # conda install -y pip ffmpeg && \
-    conda install -c conda-forge libsndfile && \
-    conda clean --all -y
-# RUN pip install torchaudio==0.11.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
+# Working directory
+WORKDIR /app
+
+# Environment variables for LocalAI (transcription/diarization)
+# Set these via docker run -e or docker-compose
+ENV LOCALAI_API_URL=http://localhost:8080
+ENV LOCALAI_API_KEY=
+ENV LOCALAI_MODEL=vibevoice-diarize
+
+# Environment variables for Summarizer LLM
+ENV SUMMARIZER_API_URL=http://localhost:8080
+ENV SUMMARIZER_API_KEY=
+ENV SUMMARIZER_MODEL=llama-3.1-8b-instruct
+
+# Copy and install Python dependencies
+COPY requirements.txt /app/requirements.txt
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Expose port
-EXPOSE 7860
-# Run the application
+# Copy application code
+COPY scraibe /app/scraibe
 
+# Expose port (if UI is served)
+EXPOSE 7860
+
+# Run the application
 ENTRYPOINT ["python3", "-m", "scraibe.cli"]

pyproject.toml

@@ -5,38 +5,42 @@ build-backend = "poetry_dynamic_versioning.backend"
 [tool.poetry]
 name = "scraibe"
 version = "0.0.0"
-description = "Transcription tool for audio files based on Whisper and Pyannote"
+description = "LocalAI-backed transcription and diarization client using vibevoice.cpp"
 authors = ["Schmieder, Jacob <jacob.schmieder@dbfz.de>"]
 license = "GPL-3.0-or-later"
 readme = ["README.md", "LICENSE"]
 repository = "https://github.com/JSchmie/ScAIbe"
 documentation = "https://jschmie.github.io/ScrAIbe/"
-keywords = ["transcription", "audio", "whisper", "pyannote", "speech-to-text", "speech-recognition"]
+keywords = [
+    "transcription",
+    "audio",
+    "diarization",
+    "localai",
+    "vibevoice",
+    "speech-to-text",
+]
 classifiers = [
-                'Development Status :: 4 - Beta',
-                'Intended Audience :: Developers',
-                'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
-                'Programming Language :: Python :: 3.8',
-                'Programming Language :: Python :: 3.9',
-                'Programming Language :: Python :: 3.10',
-                'Programming Language :: Python :: 3.11',
-                'Environment :: GPU :: NVIDIA CUDA :: 12 :: 12.1',
-                'Topic :: Scientific/Engineering :: Artificial Intelligence'
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
 packages = [{include = "scraibe"}]
 exclude = [
     "__pycache__",
     "*.pyc",
-        "test"
+    "test",
 ]
+
 [tool.poetry.dependencies]
 python = "^3.9"
 tqdm = "^4.66.5"
 numpy = "^1.26.4"
-openai-whisper = ">=20231117,<20240931"
-faster-whisper = "^1.0.3"
-"pyannote.audio" = "^3.3.1"
-torch = "^2.1.2"
+httpx = ">=0.28.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.1.1"
@@ -69,5 +73,5 @@ scraibe = "scraibe.cli:cli"
 app = ["scraibe-webui"]
 
 [tool.ruff.lint.extend-per-file-ignores]
-"__init__.py" = ["E402","F403",'F401']
+"__init__.py" = ["E402", "F403", "F401"]
 "scraibe/misc.py" = ["E722"]

requirements.txt

@@ -1,14 +1,3 @@
 tqdm>=4.66.5
 numpy>=1.26.4
-
-openai-whisper==20231117
-faster-whisper~=1.0.3
-
-pyannote.audio~=3.3.1
-pyannote.core~=5.0.0
-pyannote.database~=5.0.1 
-pyannote.metrics~=3.2.1
-pyannote.pipeline~=3.0.1
-
-torchaudio>=2.1.2
-
+httpx>=0.28.0

scraibe/__init__.py

@@ -1,11 +1,10 @@
-from .autotranscript import *
-from .transcriber import *
-from .audio import *
-from .transcript_exporter import *
-from .diarisation import *
+from .autotranscript import Scraibe
+from .localai_client import LocalAIClient, LocalAIError
+from .summarizer import SummarizerClient, SummarizerError
+from .audio import AudioProcessor
+from .transcript_exporter import Transcript
+from .misc import set_threads, ParseKwargs
 
-from .misc import *
-
-from .cli import *
+from .cli import cli
 
 from ._version import __version__

scraibe/audio.py

@@ -2,28 +2,15 @@
 Audio Processor Module
 =======================
 
-This module provides the AudioProcessor class, utilizing PyTorchaudio for handling audio files.
-It includes functionalities to load, cut, and manage audio waveforms, offering efficient and
-flexible audio processing.
+Simplified audio processor for ScrAIbe.
 
-Available Classes:
-- AudioProcessor: Processes audio waveforms and provides methods for loading, 
-                    cutting, and handling audio.
-
-Usage:
-    from .audio_import AudioProcessor
-
-    processor = AudioProcessor.from_file("path/to/audiofile.wav")
-    cut_waveform = processor.cut(start=1.0, end=5.0)
-
-Constants:
-- SAMPLE_RATE (int): Default sample rate for processing.
-- NORMALIZATION_FACTOR (float): Normalization factor for audio waveform.
+Previously this used torch and pyannote-style processing. In the LocalAI-backed
+version, we primarily pass files to the API, but we keep a lightweight helper
+for backward compatibility.
 """
 
 from subprocess import CalledProcessError, run
 import numpy as np
-import torch
 
 SAMPLE_RATE = 16000
 NORMALIZATION_FACTOR = 32768.0
@@ -31,38 +18,25 @@ NORMALIZATION_FACTOR = 32768.0
 
 class AudioProcessor:
     """
-    Audio Processor class that leverages PyTorchaudio to provide functionalities
-    for loading, cutting, and handling audio waveforms.
+    Lightweight audio processor for loading and cutting audio.
 
     Attributes:
-        waveform: torch.Tensor
-            The audio waveform tensor.
-        sr: int
-            The sample rate of the audio.
-    """
-
-    def __init__(self, waveform: torch.Tensor,
-                 sr: int = SAMPLE_RATE) -> None:
-        """
-        Initialize the AudioProcessor object.
-
-        Args:
-            waveform (torch.Tensor): The audio waveform tensor.
-            sr (int, optional): The sample rate of the audio. Defaults to SAMPLE_RATE.
-
-        Raises:
-            ValueError: If the provided sample rate is not of type int.
+        waveform (np.ndarray): The audio waveform as float32.
+        sr (int): The sample rate of the audio.
     """
 
+    def __init__(self, waveform: np.ndarray, sr: int = SAMPLE_RATE):
         self.waveform = waveform
         self.sr = sr
 
         if not isinstance(self.sr, int):
-            raise ValueError("Sample rate should be a single value of type int,"
-                             f"not {len(self.sr)} and type {type(self.sr)}")
+            raise ValueError(
+                "Sample rate should be a single value of type int, "
+                f"not {len(self.sr)} and type {type(self.sr)}"
+            )
 
     @classmethod
-    def from_file(cls, file: str, *args, **kwargs) -> 'AudioProcessor':
+    def from_file(cls, file: str, *args, **kwargs):
         """
         Create an AudioProcessor instance from an audio file.
 
@@ -70,55 +44,42 @@ class AudioProcessor:
             file (str): The audio file path.
 
         Returns:
-            AudioProcessor: An instance of the AudioProcessor class containing the loaded audio.
+            AudioProcessor: Instance with loaded audio.
         """
-
         audio, sr = cls.load_audio(file, *args, **kwargs)
-
-        audio = torch.from_numpy(audio)
-
         return cls(audio, sr)
 
-    def cut(self, start: float, end: float) -> torch.Tensor:
+    def cut(self, start: float, end: float) -> np.ndarray:
         """
-        Cut a segment from the audio waveform between the specified start and end times.
+        Cut a segment from the audio waveform.
 
         Args:
             start (float): Start time in seconds.
             end (float): End time in seconds.
 
         Returns:
-            torch.Tensor: The cut waveform segment.
+            np.ndarray: The cut waveform segment.
         """
-
-        start = int(start * self.sr)
-        if (isinstance(end, float) or isinstance(end, int)) and isinstance(self.sr, int):
-            end = int(np.ceil(end * self.sr))
-        else:
-            end = int(torch.ceil(end * self.sr))
-        return self.waveform[start:end]
+        start_idx = int(start * self.sr)
+        end_idx = int(np.ceil(end * self.sr))
+        return self.waveform[start_idx:end_idx]
 
     @staticmethod
     def load_audio(file: str, sr: int = SAMPLE_RATE):
         """
-        Open an audio file and read it as a mono waveform, resampling if necessary.
-        This method ensures compatibility with pyannote.audio
-        and requires the ffmpeg CLI in PATH.
+        Load an audio file as a mono waveform, resampling if necessary.
+        Requires ffmpeg in PATH.
 
         Args:
             file (str): The audio file to open.
-            sr (int, optional): The desired sample rate. Defaults to SAMPLE_RATE.
+            sr (int, optional): The desired sample rate.
 
         Returns:
-            tuple: A NumPy array containing the audio waveform in float32 dtype
-                    and the sample rate.
+            tuple: (waveform as np.ndarray[float32], sample rate)
 
         Raises:
             RuntimeError: If failed to load audio.
         """
-        # This launches a subprocess to decode audio while down-mixing
-        # and resampling as necessary.  Requires the ffmpeg CLI in PATH.
-        # fmt: off
         cmd = [
             "ffmpeg",
             "-nostdin",
@@ -128,19 +89,20 @@ class AudioProcessor:
             "-ac", "1",
             "-acodec", "pcm_s16le",
             "-ar", str(sr),
-            "-"
+            "-",
         ]
-        # fmt: on
         try:
             out = run(cmd, capture_output=True, check=True).stdout
         except CalledProcessError as e:
             raise RuntimeError(
-                f"Failed to load audio: {e.stderr.decode()}") from e
+                f"Failed to load audio: {e.stderr.decode()}"
+            ) from e
 
-        out = np.frombuffer(out, np.int16).flatten().astype(
-            np.float32) / NORMALIZATION_FACTOR
+        waveform = np.frombuffer(out, np.int16).flatten().astype(
+            np.float32
+        ) / NORMALIZATION_FACTOR
 
-        return out, sr
+        return waveform, sr
 
     def __repr__(self) -> str:
-        return f'TorchAudioProcessor(waveform={len(self.waveform)}, sr={int(self.sr)})'
+        return f"AudioProcessor(waveform_len={len(self.waveform)}, sr={self.sr})"

scraibe/autotranscript.py

@@ -1,358 +1,281 @@
 """
-Scraibe Class
---------------------
+Scraibe Class (LocalAI-backed)
+------------------------------
 
-This class serves as the core of the transcription system, responsible for handling
-transcription and diarization of audio files. It leverages pretrained models for
-speech-to-text (such as Whisper) and speaker diarization (such as pyannote.audio),
-providing an accessible interface for audio processing tasks such as transcription,
-speaker separation, and timestamping.
+Core class for transcription and (optionally) summarization.
 
-By encapsulating the complexities of underlying models, it allows for straightforward
-integration into various applications, ranging from transcription services to voice assistants.
+- Transcription and diarization are delegated to LocalAI (vibevoice.cpp).
+- Summarization is delegated to a separate LLM via /v1/chat/completions.
 
-Available Classes:
-- Scraibe: Main class for performing transcription and diarization.
-                  Includes methods for loading models, processing audio files,
-                  and formatting the transcription output.
+Public tasks:
+- transcribe
+- transcript_and_summarize (transcribe + generate a detailed summary)
 
-Usage:
-    from scraibe import Scraibe
-
-    model = Scraibe()
-    transcript = model.autotranscribe("path/to/audiofile.wav")
+Previous task/whisper/pyannote-specific settings are kept for compatibility
+but ignored when not relevant.
 """
 
-# Standard Library Imports
 import os
-from glob import iglob
-from subprocess import run
-from typing import TypeVar, Union
-from warnings import warn
+from typing import Union, Optional
 
-# Third-Party Imports
-import torch
-from numpy import ndarray
-from tqdm import trange
-
-# Application-Specific Imports
-from .audio import AudioProcessor
-from .diarisation import Diariser
-from .transcriber import Transcriber, load_transcriber, whisper
+from .localai_client import LocalAIClient, LocalAIError
+from .summarizer import SummarizerClient, SummarizerError
 from .transcript_exporter import Transcript
-from .misc import SCRAIBE_TORCH_DEVICE
-
-
-DiarisationType = TypeVar('DiarisationType')
 
 
 class Scraibe:
     """
-    Scraibe is a class responsible for managing the transcription and diarization of audio files.
-    It serves as the core of the transcription system, incorporating pretrained models
-    for speech-to-text (such as Whisper) and speaker diarization (such as pyannote.audio),
-    allowing for comprehensive audio processing.
+    Scraibe now:
+    - Uses LocalAI for transcription + diarization.
+    - Uses a separate LLM for summarization (when requested).
 
-    Attributes:
-        transcriber (Transcriber): The transcriber object to handle transcription.
-        diariser (Diariser): The diariser object to handle diarization.
-
-    Methods:
-        __init__: Initializes the Scraibe class with appropriate models.
-        transcribe: Transcribes an audio file using the whisper model and pyannote diarization model.
-        remove_audio_file: Removes the original audio file to avoid disk space issues or ensure data privacy.
-        get_audio_file: Gets an audio file as an AudioProcessor object.
+    Public methods:
+    - transcribe(audio_file, ...)
+    - transcript_and_summarize(audio_file, ...)
     """
 
-    def __init__(self,
-                 whisper_model: Union[bool, str, whisper] = None,
+    def __init__(
+        self,
+        api_url: Optional[str] = None,
+        api_key: Optional[str] = None,
+        model: Optional[str] = None,
+        whisper_model: Union[bool, str] = None,
         whisper_type: str = "whisper",
-                 dia_model: Union[bool, str, DiarisationType] = None,
-                 **kwargs) -> None:
-        """Initializes the Scraibe class.
+        dia_model: Union[bool, str] = None,
+        use_auth_token: str = None,
+        verbose: bool = False,
+        **kwargs,
+    ) -> None:
+        """
+        Initialize Scraibe with LocalAI client and summarizer client.
 
         Args:
-            whisper_model (Union[bool, str, whisper], optional): 
-                                Path to whisper model or whisper model itself.
-            whisper_type (str):
-                                Type of whisper model to load. "whisper" or "faster-whisper".
-            diarisation_model (Union[bool, str, DiarisationType], optional): 
-                                Path to pyannote diarization model or model itself.
-            **kwargs: Additional keyword arguments for whisper
-                        and pyannote diarization models.
-                    e.g.:
+            api_url: LocalAI server URL for transcription/diarization.
+                     Falls back to LOCALAI_API_URL env var.
+            api_key: API key for LocalAI. Falls back to LOCALAI_API_KEY.
+            model: Model name for LocalAI (e.g., vibevoice-diarize).
+                   Falls back to LOCALAI_MODEL env var.
 
-                    - verbose: If True, the class will print additional information.
-                    - save_kwargs: If True, the keyword arguments will be saved
-                                    for autotranscribe. So you can unload the class and reload it again.
+            Summarizer uses:
+            - SUMMARIZER_API_URL
+            - SUMMARIZER_API_KEY
+            - SUMMARIZER_MODEL
+            These can be overridden via environment or via the transcript_and_summarize
+            method if needed.
+
+            Backward-compat (ignored):
+            - whisper_model, whisper_type, dia_model, use_auth_token, etc.
         """
+        self.verbose = verbose or kwargs.get("verbose", False)
 
-        if whisper_model is None:
-            self.transcriber = load_transcriber(
-                "medium", whisper_type, **kwargs)
-        elif isinstance(whisper_model, str):
-            self.transcriber = load_transcriber(
-                whisper_model, whisper_type, **kwargs)
-        else:
-            self.transcriber = whisper_model
+        try:
+            self.client = LocalAIClient(
+                api_url=api_url,
+                api_key=api_key,
+                model=model,
+            )
+        except LocalAIError as e:
+            raise LocalAIError(f"Failed to initialize LocalAI client: {e}")
 
-        if dia_model is None:
-            self.diariser = Diariser.load_model(**kwargs)
-        elif isinstance(dia_model, str):
-            self.diariser = Diariser.load_model(dia_model, **kwargs)
-        else:
-            self.diariser: Diariser = dia_model
-
-        if kwargs.get("verbose"):
-            print("Scraibe initialized all models successfully loaded.")
-            self.verbose = True
-        else:
-            self.verbose = False
-
-        # Save kwargs for autotranscribe if you want to unload the class and load it again.
-        if kwargs.get('save_setup'):
-            self.params = dict(whisper_model=whisper_model,
-                               dia_model=dia_model,
-                               **kwargs)
-        else:
-            self.params = {}
-            
-        self.device = kwargs.get(
-            "device", SCRAIBE_TORCH_DEVICE)
-
-    def autotranscribe(self, audio_file: Union[str, torch.Tensor, ndarray],
-                       remove_original: bool = False,
-                       **kwargs) -> Transcript:
-        """
-        Transcribes an audio file using the whisper model and pyannote diarization model.
-
-        Args:
-            audio_file (Union[str, torch.Tensor, ndarray]): 
-                            Path to audio file or a tensor representing the audio.
-            remove_original (bool, optional): If True, the original audio file will
-                                                be removed after transcription.
-            *args: Additional positional arguments for diarization and transcription.
-            **kwargs: Additional keyword arguments for diarization and transcription.
-
-        Returns:
-            Transcript: A Transcript object containing the transcription,
-                        which can be exported to different formats.
-        """
-        if kwargs.get("verbose"):
-            self.verbose = kwargs.get("verbose")
-        # Get audio file as an AudioProcessor object
-        audio_file: AudioProcessor = self.get_audio_file(audio_file)
-
-        # Prepare waveform and sample rate for diarization
-        dia_audio = {
-            "waveform": audio_file.waveform.reshape(1, len(audio_file.waveform)).to(self.device),
-            "sample_rate": audio_file.sr
-        }
+        # Summarizer is lazy-initialized if needed
+        self._summarizer: Optional[SummarizerClient] = None
 
         if self.verbose:
-            print("Starting diarisation.")
+            print("Scraibe initialized. Using LocalAI for transcription and diarization.")
 
-        diarisation = self.diariser.diarization(dia_audio, **kwargs)
-
-        if not diarisation["segments"]:
-            print("No segments found. Try to run transcription without diarisation.")
-
-            transcript = self.transcriber.transcribe(
-                audio_file.waveform, **kwargs)
-
-            final_transcript = {0: {"speakers": 'SPEAKER_01',
-                                    "segments": [0, len(audio_file.waveform)],
-                                    "text": transcript}}
-
-            return Transcript(final_transcript)
-
-        if self.verbose:
-            print("Diarisation finished. Starting transcription.")
-
-
-        # Transcribe each segment and store the results
-        final_transcript = dict()
-
-        for i in trange(len(diarisation["segments"]), desc="Transcribing", disable=not self.verbose):
-
-            seg = diarisation["segments"][i]
-
-            audio = audio_file.cut(seg[0], seg[1])
-
-            transcript = self.transcriber.transcribe(audio, **kwargs)
-
-            final_transcript[i] = {"speakers": diarisation["speakers"][i],
-                                   "segments": seg,
-                                   "text": transcript}
-
-        # Remove original file if needed
-        if remove_original:
-            if kwargs.get("shred") is True:
-                self.remove_audio_file(audio_file, shred=True)
-            else:
-                self.remove_audio_file(audio_file, shred=False)
-
-        return Transcript(final_transcript)
-
-    def diarization(self, audio_file: Union[str, torch.Tensor, ndarray],
-                    **kwargs) -> dict:
+    def _ensure_summarizer(
+        self,
+        api_url: Optional[str] = None,
+        api_key: Optional[str] = None,
+        model: Optional[str] = None,
+    ) -> SummarizerClient:
         """
-        Perform diarization on an audio file using the pyannote diarization model.
+        Lazy-init summarizer client.
+        """
+        if self._summarizer is not None:
+            return self._summarizer
+
+        try:
+            self._summarizer = SummarizerClient(
+                api_url=api_url,
+                api_key=api_key,
+                model=model,
+            )
+        except SummarizerError as e:
+            raise SummarizerError(f"Failed to initialize Summarizer client: {e}")
+
+        return self._summarizer
+
+    # -----------------
+    # Primary public API
+    # -----------------
+
+    def transcribe(
+        self,
+        audio_file: Union[str],
+        **kwargs,
+    ) -> str:
+        """
+        Transcribe the provided audio file using LocalAI.
+
+        Uses /v1/audio/diarization with vibevoice.cpp, then concatenates
+        all segment texts.
 
         Args:
-            audio_file (Union[str, torch.Tensor, ndarray]):
-                The audio source which can either be a path to the audio file or a tensor representation.
-            **kwargs: 
-                Additional keyword arguments for diarization.
+            audio_file (str): Path to the audio file.
+            **kwargs: Additional keyword arguments (some forwarded, others ignored).
 
         Returns:
-            dict: 
-                A dictionary containing the results of the diarization process.
+            str: The concatenated transcribed text.
         """
+        if isinstance(audio_file, str):
+            if not os.path.exists(audio_file):
+                raise FileNotFoundError(f"Audio file not found: {audio_file}")
+        else:
+            raise TypeError(
+                "In LocalAI mode, audio_file must be a file path (str)."
+            )
 
-        # Get audio file as an AudioProcessor object
-        audio_file: AudioProcessor = self.get_audio_file(audio_file)
+        verbose = kwargs.get("verbose", self.verbose)
 
-        # Prepare waveform and sample rate for diarization
-        dia_audio = {
-            "waveform": audio_file.waveform.reshape(1, len(audio_file.waveform)).to(self.device),
-            "sample_rate": audio_file.sr
+        try:
+            result = self.client.diarize_and_transcribe(
+                audio_path=audio_file,
+                include_text=True,
+                verbose=verbose,
+                **kwargs,
+            )
+        except LocalAIError as e:
+            raise LocalAIError(f"Error during LocalAI transcription: {e}")
+
+        transcripts = result.get("transcripts", [])
+        return " ".join(t.strip() for t in transcripts if t.strip())
+
+    def transcript_and_summarize(
+        self,
+        audio_file: Union[str],
+        *,
+        summarizer_api_url: Optional[str] = None,
+        summarizer_api_key: Optional[str] = None,
+        summarizer_model: Optional[str] = None,
+        **kwargs,
+    ) -> dict:
+        """
+        Transcribe the audio file and generate a detailed summary.
+
+        Steps:
+        - Transcribe via LocalAI.
+        - Build a plain-text transcript (with speaker labels).
+        - Summarize the transcript using the configured LLM.
+
+        Returns:
+            dict with:
+              - transcript: full transcript text (with speaker labels)
+              - summary: final detailed summary (markdown-ready)
+        """
+        if isinstance(audio_file, str):
+            if not os.path.exists(audio_file):
+                raise FileNotFoundError(f"Audio file not found: {audio_file}")
+        else:
+            raise TypeError(
+                "In LocalAI mode, audio_file must be a file path (str)."
+            )
+
+        verbose = kwargs.get("verbose", self.verbose)
+
+        # 1) Get diarized + transcribed result
+        try:
+            result = self.client.diarize_and_transcribe(
+                audio_path=audio_file,
+                include_text=True,
+                verbose=verbose,
+                **kwargs,
+            )
+        except LocalAIError as e:
+            raise LocalAIError(f"Error during LocalAI transcription: {e}")
+
+        segments = result.get("segments", [])
+        speakers = result.get("speakers", [])
+        transcripts = result.get("transcripts", [])
+
+        if not segments:
+            return {
+                "transcript": "",
+                "summary": "No transcript content to summarize.",
             }
 
-        print("Starting diarisation.")
+        # 2) Build full transcript text with speaker labels
+        lines = []
+        for seg, speaker, text in zip(segments, speakers, transcripts):
+            start, end = seg
+            ts = self._format_timestamp(start)
+            line = f"[{ts}] {speaker}: {text.strip()}"
+            lines.append(line)
 
-        diarisation = self.diariser.diarization(dia_audio, **kwargs)
+        full_transcript = "\n\n".join(lines)
 
-        return diarisation
+        # 3) Summarize
+        try:
+            summarizer = self._ensure_summarizer(
+                api_url=summarizer_api_url,
+                api_key=summarizer_api_key,
+                model=summarizer_model,
+            )
+        except SummarizerError as e:
+            raise SummarizerError(f"Failed to initialize summarizer: {e}")
 
-    def transcribe(self, audio_file: Union[str, torch.Tensor, ndarray],
-                   **kwargs):
-        """
-            Transcribe the provided audio file.
+        try:
+            summary = summarizer.summarize_transcript(full_transcript)
+        except SummarizerError as e:
+            raise SummarizerError(f"Error during summarization: {e}")
 
-            Args:
-                audio_file (Union[str, torch.Tensor, ndarray]):
-                    The audio source, which can either be a path or a tensor representation.
-                **kwargs: 
-                    Additional keyword arguments for transcription.
+        return {
+            "transcript": full_transcript,
+            "summary": summary,
+        }
 
-            Returns:
-                str:
-                    The transcribed text from the audio source.
-        """
-        audio_file: AudioProcessor = self.get_audio_file(audio_file)
-
-        return self.transcriber.transcribe(audio_file.waveform, **kwargs)
-
-    def update_transcriber(self, whisper_model: Union[str, whisper], **kwargs) -> None:
-        """
-        Update the transcriber model.
-
-        Args:
-            whisper_model (Union[str, whisper]):
-                The new whisper model to use for transcription.
-            **kwargs:
-                Additional keyword arguments for the transcriber model.
-
-            Returns:
-                None
-        """
-        _old_model = self.transcriber.model_name
-
-        if isinstance(whisper_model, str):
-            self.transcriber = load_transcriber(whisper_model, **kwargs)
-        elif isinstance(whisper_model, Transcriber):
-            self.transcriber = whisper_model
-        else:
-            warn(
-                f"Invalid model type. Please provide a valid model. Fallback to old {_old_model} Model.", RuntimeWarning)
-
-        return None
-
-    def update_diariser(self, dia_model: Union[str, DiarisationType], **kwargs) -> None:
-        """
-        Update the diariser model.
-
-        Args:
-            dia_model (Union[str, DiarisationType]):
-                The new diariser model to use for diarization.
-            **kwargs:
-                Additional keyword arguments for the diariser model.
-
-            Returns:
-                None
-        """
-        if isinstance(dia_model, str):
-            self.diariser = Diariser.load_model(dia_model, **kwargs)
-        elif isinstance(dia_model, Diariser):
-            self.diariser = dia_model
-        else:
-            warn("Invalid model type. Please provide a valid model. Fallback to old Model.", RuntimeWarning)
-
-        return None
+    # -----------------
+    # Helpers
+    # -----------------
 
     @staticmethod
-    def remove_audio_file(audio_file: str,
-                          shred: bool = False) -> None:
+    def _format_timestamp(seconds: float) -> str:
         """
-        Removes the original audio file to avoid disk space issues or ensure data privacy.
+        Format seconds into MM:SS or HH:MM:SS.
+        """
+        m, s = divmod(int(seconds), 60)
+        h, m = divmod(m, 60)
+        if h > 0:
+            return f"{h:02d}:{m:02d}:{s:02d}"
+        return f"{m:02d}:{s:02d}"
 
-        Args:
-            audio_file_path (str): Path to the audio file.
-            shred (bool, optional): If True, the audio file will be shredded,
-                                    not just removed.
+    @staticmethod
+    def remove_audio_file(audio_file: str, shred: bool = False) -> None:
+        """
+        Remove the original audio file.
         """
         if not os.path.exists(audio_file):
             raise ValueError(f"Audiofile {audio_file} does not exist.")
 
         if shred:
+            import subprocess
+            import warnings
+            from glob import iglob
 
-            warn("Shredding audiofile can take a long time.", RuntimeWarning)
+            warnings.warn("Shredding audiofile can take a long time.", RuntimeWarning)
 
-            gen = iglob(f'{audio_file}', recursive=True)
-            cmd = ['shred', '-zvu', '-n', '10', f'{audio_file}']
+            gen = iglob(f"{audio_file}", recursive=True)
+            cmd = ["shred", "-zvu", "-n", "10", f"{audio_file}"]
 
             if os.path.isdir(audio_file):
                 raise ValueError(f"Audiofile {audio_file} is a directory.")
 
             for file in gen:
-                print(f'shredding {file} now\n')
-
-                run(cmd, check=True)
-
+                print(f"shredding {file} now\n")
+                subprocess.run(cmd, check=True)
         else:
             os.remove(audio_file)
             print(f"Audiofile {audio_file} removed.")
 
-    @staticmethod
-    def get_audio_file(audio_file: Union[str, torch.Tensor, ndarray]) -> AudioProcessor:
-        """Gets an audio file as TorchAudioProcessor.
-
-        Args:
-            audio_file (Union[str, torch.Tensor, ndarray]): Path to the audio file or 
-                                                        a tensor representing the audio.
-            *args: Additional positional arguments.
-            **kwargs: Additional keyword arguments.
-
-        Returns:
-            AudioProcessor: An object containing the waveform and sample rate in
-                            torch.Tensor format.
-        """
-
-        if isinstance(audio_file, str):
-            audio_file = AudioProcessor.from_file(audio_file)
-
-        elif isinstance(audio_file, torch.Tensor):
-            audio_file = AudioProcessor(audio_file[0], audio_file[1])
-        elif isinstance(audio_file, ndarray):
-            audio_file = AudioProcessor(torch.Tensor(audio_file[0]),
-                                        audio_file[1])
-
-        if not isinstance(audio_file, AudioProcessor):
-            raise ValueError(f'Audiofile must be of type AudioProcessor,'
-                             f'not {type(audio_file)}')
-
-        return audio_file
-
     def __repr__(self):
-        return f"Scraibe(transcriber={self.transcriber}, diariser={self.diariser})"
+        return "Scraibe(LocalAI-backed)"

scraibe/cli.py

@@ -3,23 +3,21 @@ Command-Line Interface (CLI) for the Scraibe class,
 allowing for user interaction to transcribe and diarize audio files.
 The function includes arguments for specifying the audio files, model paths,
 output formats, and other options necessary for transcription.
+
+This version is adapted for LocalAI-based transcription and diarization.
 """
+
 import os
 import json
 from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
-from whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE
-from torch.cuda import is_available
 from .autotranscript import Scraibe
 from .misc import set_threads
 
+
 def cli():
     """
     Command-Line Interface (CLI) for the Scraibe class, allowing for user interaction to transcribe
-    and diarize audio files. The function includes arguments for specifying the audio files, model paths, 
-    output formats, and other options necessary for transcription.
-
-    This function can be executed from the command line to perform transcription tasks, providing a 
-    user-friendly way to access the Scraibe class functionalities.
+    and diarize audio files via a LocalAI server.
     """
 
     def str2bool(string):
@@ -28,59 +26,160 @@ def cli():
             return str2val[string]
         else:
             raise ValueError(
-                f"Expected one of {set(str2val.keys())}, got {string}")
+                f"Expected one of {set(str2val.keys())}, got {string}"
+            )
 
     parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
 
-    parser.add_argument("-f", "--audio-files", nargs="+", type=str, default=None,
-                        help="List of audio files to transcribe.")
+    parser.add_argument(
+        "-f",
+        "--audio-files",
+        nargs="+",
+        type=str,
+        default=None,
+        help="List of audio files to transcribe.",
+    )
 
-    parser.add_argument("--whisper-type", type=str, default="whisper",
+    # LocalAI connection (env vars preferred, but CLI overrides allowed)
+    parser.add_argument(
+        "--localai-api-url",
+        type=str,
+        default=None,
+        help="LocalAI server URL (e.g., http://localhost:8080). "
+             "Overrides LOCALAI_API_URL env var if provided.",
+    )
+    parser.add_argument(
+        "--localai-api-key",
+        type=str,
+        default=None,
+        help="LocalAI API key. Overrides LOCALAI_API_KEY env var if provided.",
+    )
+    parser.add_argument(
+        "--localai-model",
+        type=str,
+        default=None,
+        help="Model name to use on LocalAI (e.g., vibevoice-diarize). "
+             "Overrides LOCALAI_MODEL env var if provided.",
+    )
+
+    # Summarizer overrides (env vars are primary)
+    parser.add_argument(
+        "--summarizer-api-url",
+        type=str,
+        default=None,
+        help="Summarization LLM API URL (e.g., http://localhost:8080). "
+             "Overrides SUMMARIZER_API_URL env var if provided.",
+    )
+    parser.add_argument(
+        "--summarizer-api-key",
+        type=str,
+        default=None,
+        help="Summarization LLM API key. Overrides SUMMARIZER_API_KEY env var if provided.",
+    )
+    parser.add_argument(
+        "--summarizer-model",
+        type=str,
+        default=None,
+        help="Model name for summarization. Overrides SUMMARIZER_MODEL env var if provided.",
+    )
+
+    # Kept for backward compatibility with UI / existing scripts; ignored by LocalAI client.
+    parser.add_argument(
+        "--whisper-type",
+        type=str,
+        default="whisper",
         choices=["whisper", "faster-whisper"],
-                        help="Type of Whisper model to use ('whisper' or 'faster-whisper').")
+        help="[Backward compatibility] Type of Whisper model. Ignored when using LocalAI.",
+    )
 
-    parser.add_argument("--whisper-model-name", default="medium",
-                        help="Name of the Whisper model to use.")
+    parser.add_argument(
+        "--whisper-model-name",
+        default="medium",
+        help="[Backward compatibility] Whisper model name. Ignored when using LocalAI.",
+    )
 
-    parser.add_argument("--whisper-model-directory", type=str, default=None,
-                        help="Path to save Whisper model files; defaults to ./models/whisper.")
+    parser.add_argument(
+        "--whisper-model-directory",
+        type=str,
+        default=None,
+        help="[Backward compatibility] Whisper model directory. Ignored when using LocalAI.",
+    )
 
-    parser.add_argument("--diarization-directory", type=str, default=None,
-                        help="Path to the diarization model directory.")
+    parser.add_argument(
+        "--diarization-directory",
+        type=str,
+        default=None,
+        help="[Backward compatibility] Diarization model directory. Ignored when using LocalAI.",
+    )
 
-    parser.add_argument("--hf-token", default=None, type=str,
-                        help="HuggingFace token for private model download.")
+    parser.add_argument(
+        "--hf-token",
+        default=None,
+        type=str,
+        help="[Backward compatibility] HuggingFace token. Ignored when using LocalAI.",
+    )
 
-    parser.add_argument("--inference-device",
-                        default="cuda" if is_available() else "cpu",
-                        help="Device to use for PyTorch inference.")
+    parser.add_argument(
+        "--inference-device",
+        default="cpu",
+        help="[Backward compatibility] Device for inference. Ignored when using LocalAI.",
+    )
 
-    parser.add_argument("--num-threads", type=int, default=None,
-                        help="Number of threads used by torch for CPU inference; '\
-                            'overrides MKL_NUM_THREADS/OMP_NUM_THREADS.")
+    parser.add_argument(
+        "--num-threads",
+        type=int,
+        default=None,
+        help="Number of threads used for CPU operations; overrides MKL_NUM_THREADS/OMP_NUM_THREADS.",
+    )
 
-    parser.add_argument("--output-directory", "-o", type=str, default=".",
-                        help="Directory to save the transcription outputs.")
+    parser.add_argument(
+        "--output-directory",
+        "-o",
+        type=str,
+        default=".",
+        help="Directory to save the transcription outputs.",
+    )
 
-    parser.add_argument("--output-format", "-of", type=str, default="txt",
+    parser.add_argument(
+        "--output-format",
+        "-of",
+        type=str,
+        default="txt",
         choices=["txt", "json", "md", "html"],
-                        help="Format of the output file; defaults to txt.")
+        help="Format of the output file; defaults to txt.",
+    )
 
-    parser.add_argument("--verbose-output", type=str2bool, default=True,
-                        help="Enable or disable progress and debug messages.")
+    parser.add_argument(
+        "--verbose-output",
+        type=str2bool,
+        default=True,
+        help="Enable or disable progress and debug messages.",
+    )
 
-    parser.add_argument("--task", type=str, default='autotranscribe',
-                        choices=["autotranscribe", "diarization",
-                                 "autotranscribe+translate", "translate", 'transcribe'],
-                        help="Choose to perform transcription, diarization, or translation. \
-                        If set to translate, the output will be translated to English.")
+    parser.add_argument(
+        "--task",
+        type=str,
+        default="transcribe",
+        choices=[
+            "transcribe",
+            "transcript_and_summarize",
+        ],
+        help="Task to perform: 'transcribe' or 'transcript_and_summarize'.",
+    )
 
-    parser.add_argument("--language", type=str, default=None,
-                        choices=sorted(
-                            LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]),
-                        help="Language spoken in the audio. Specify None to perform language detection.")
-    parser.add_argument("--num-speakers", type=int, default=2,
-                        help="Number of speakers in the audio.")
+    parser.add_argument(
+        "--language",
+        type=str,
+        default=None,
+        help="Language spoken in the audio. Specify None to perform language detection.",
+    )
+
+    parser.add_argument(
+        "--num-speakers",
+        type=int,
+        default=None,
+        help="Number of speakers in the audio.",
+    )
 
     args = parser.parse_args()
 
@@ -96,65 +195,64 @@ def cli():
 
     set_threads(arg_dict.pop("num_threads"))
 
-    class_kwargs = {'whisper_model': arg_dict.pop("whisper_model_name"),
-                    'whisper_type':arg_dict.pop("whisper_type"),
-                    'dia_model': arg_dict.pop("diarization_directory"),
-                    'use_auth_token': arg_dict.pop("hf_token"),
+    # Build kwargs for Scraibe (LocalAI-backed)
+    class_kwargs = {
+        "api_url": arg_dict.pop("localai_api_url"),
+        "api_key": arg_dict.pop("localai_api_key"),
+        "model": arg_dict.pop("localai_model"),
+        # kept for backward compatibility, but ignored:
+        "whisper_model": arg_dict.pop("whisper_model_name"),
+        "whisper_type": arg_dict.pop("whisper_type"),
+        "dia_model": arg_dict.pop("diarization_directory"),
+        "use_auth_token": arg_dict.pop("hf_token"),
+        "verbose": arg_dict.pop("verbose_output"),
     }
 
-    if arg_dict["whisper_model_directory"]:
-        class_kwargs["download_root"] = arg_dict.pop("whisper_model_directory")
-        
-
     model = Scraibe(**class_kwargs)
 
     if arg_dict["audio_files"]:
         audio_files = arg_dict.pop("audio_files")
 
-        if task == "autotranscribe" or task == "autotranscribe+translate":
+        if task == "transcribe":
             for audio in audio_files:
-                if task == "autotranscribe+translate":
-                    task = "translate"
-                else:
-                    task = "transcribe"
-
-                out = model.autotranscribe(
+                out = model.transcribe(
                     audio,
-                        task=task, 
                     language=arg_dict.pop("language"),
                     verbose=arg_dict.pop("verbose_output"),
-                        num_speakers=arg_dict.pop("num_speakers")
+                    num_speakers=arg_dict.pop("num_speakers"),
                 )
                 basename = audio.split("/")[-1].split(".")[0]
-                print(f'Saving {basename}.{out_format} to {out_folder}')
-                out.save(os.path.join(
-                    out_folder, f"{basename}.{out_format}"))
-
-        elif task == "diarization":
-            for audio in audio_files:
-                if arg_dict.pop("verbose_output"):
-                    print("Verbose not implemented for diarization.")
-
-                out = model.diarization(audio)
-                basename = audio.split("/")[-1].split(".")[0]
                 path = os.path.join(out_folder, f"{basename}.{out_format}")
-
-                print(f'Saving {basename}.{out_format} to {out_folder}')
-
-                with open(path, "w") as f:
-                    json.dump(json.dumps(out, indent=1), f)
-
-        elif task == "transcribe" or task == "translate":
-
-            for audio in audio_files:
-
-                out = model.transcribe(audio, task=task,
-                                        language=arg_dict.pop("language"),
-                                        verbose=arg_dict.pop("verbose_output"))
-                basename = audio.split("/")[-1].split(".")[0]
-                path = os.path.join(out_folder, f"{basename}.{out_format}")
-                with open(path, "w") as f:
+                print(f"Saving {basename}.{out_format} to {out_folder}")
+                with open(path, "w", encoding="utf-8") as f:
                     f.write(out)
 
+        elif task == "transcript_and_summarize":
+            for audio in audio_files:
+                result = model.transcript_and_summarize(
+                    audio,
+                    summarizer_api_url=arg_dict.pop("summarizer_api_url"),
+                    summarizer_api_key=arg_dict.pop("summarizer_api_key"),
+                    summarizer_model=arg_dict.pop("summarizer_model"),
+                    language=arg_dict.pop("language"),
+                    verbose=arg_dict.pop("verbose_output"),
+                    num_speakers=arg_dict.pop("num_speakers"),
+                )
+
+                transcript_text = result.get("transcript", "")
+                summary_text = result.get("summary", "")
+
+                basename = audio.split("/")[-1].split(".")[0]
+
+                # Always use .md for transcript_and_summarize
+                md_path = os.path.join(out_folder, f"{basename}.md")
+                print(f"Saving {basename}.md (transcript + summary) to {out_folder}")
+
+                with open(md_path, "w", encoding="utf-8") as f:
+                    f.write("# Transcript\n\n")
+                    f.write(transcript_text)
+                    f.write("\n\n# Summary\n\n")
+                    f.write(summary_text)
+
 if __name__ == "__main__":
     cli()

scraibe/localai_client.py

@@ -0,0 +1,237 @@
+"""
+LocalAI Client Module
+---------------------
+
+This module provides a client for communicating with a LocalAI server
+running vibevoice.cpp for transcription and speaker diarization.
+
+It replaces the previous local Whisper + Pyannote pipeline by sending
+audio files to the /v1/audio/diarization endpoint and mapping the
+response into the same Transcript format used by the UI.
+
+Environment Variables:
+    LOCALAI_API_URL: (required) Base URL of the LocalAI server
+                     (e.g., http://localhost:8080)
+    LOCALAI_API_KEY: (optional) API key, if configured
+    LOCALAI_MODEL:   (optional) Model name to use (default: vibevoice-diarize)
+"""
+
+import os
+import io
+import json
+from typing import Dict, List, Any, Optional
+
+import httpx
+
+
+class LocalAIError(Exception):
+    """Raised when the LocalAI API returns an error or unexpected response."""
+    pass
+
+
+class LocalAIClient:
+    """
+    Thin HTTP client for LocalAI /v1/audio/diarization with vibevoice.cpp.
+
+    Responsibilities:
+      - Read configuration from environment.
+      - Upload audio file as multipart/form-data.
+      - Parse diarization + transcription response.
+      - Map response into the same structure expected by Scraibe's Transcript.
+    """
+
+    def __init__(
+        self,
+        api_url: Optional[str] = None,
+        api_key: Optional[str] = None,
+        model: Optional[str] = None,
+        timeout: float = 600.0,
+    ):
+        """
+        Args:
+            api_url: LocalAI server URL (e.g., http://localhost:8080).
+                     Falls back to LOCALAI_API_URL env var.
+            api_key: API key, if required. Falls back to LOCALAI_API_KEY.
+            model:   Model name (e.g., vibevoice-diarize).
+                     Falls back to LOCALAI_MODEL or default.
+            timeout: Request timeout in seconds.
+        """
+        self.api_url = (api_url or os.getenv("LOCALAI_API_URL")).strip().rstrip("/")
+        self.api_key = api_key or os.getenv("LOCALAI_API_KEY") or None
+        self.model = model or os.getenv("LOCALAI_MODEL") or "vibevoice-diarize"
+        self.timeout = timeout
+
+        if not self.api_url:
+            raise LocalAIError(
+                "LOCALAI_API_URL is not set. "
+                "Provide the LocalAI server URL via environment or constructor."
+            )
+
+        self._client = httpx.Client(
+            base_url=self.api_url,
+            timeout=self.timeout,
+            follow_redirects=True,
+        )
+
+    def close(self):
+        """Close the underlying HTTP client."""
+        self._client.close()
+
+    def __del__(self):
+        try:
+            self._client.close()
+        except Exception:
+            pass
+
+    def diarize_and_transcribe(
+        self,
+        audio_path: str,
+        *,
+        language: Optional[str] = None,
+        num_speakers: Optional[int] = None,
+        min_speakers: Optional[int] = None,
+        max_speakers: Optional[int] = None,
+        clustering_threshold: Optional[float] = None,
+        min_duration_on: Optional[float] = None,
+        min_duration_off: Optional[float] = None,
+        response_format: Optional[str] = None,
+        include_text: Optional[bool] = None,
+        verbose: bool = False,
+        **_ignored,
+    ) -> Dict[str, Any]:
+        """
+        Send audio to LocalAI /v1/audio/diarization and return a dict
+        in the same style as the previous internal diarization output:
+
+        {
+          "segments": [ [start, end], ... ],
+          "speakers": [ "SPEAKER_00", ... ],
+          "transcripts": [ "text for segment", ... ]
+        }
+
+        Extra kwargs that the old UI used (e.g., whisper-specific) are
+        accepted but ignored.
+
+        Args:
+            audio_path: Path to the audio file.
+            language: Language hint, forwarded if set.
+            num_speakers: Optional exact speaker count.
+            min_speakers: Optional hint.
+            max_speakers: Optional hint.
+            clustering_threshold: Optional clustering threshold.
+            min_duration_on: Optional min segment duration.
+            min_duration_off: Optional min gap duration.
+            response_format: "json", "verbose_json", or "rttm".
+                             Defaults to "verbose_json" if not set.
+            include_text: Whether to request per-segment text.
+                          Defaults to True.
+            verbose: If True, prints progress messages.
+        """
+        if verbose:
+            print("Starting diarization and transcription via LocalAI.")
+
+        # Defaults: use verbose_json + include_text to get both diarization and transcription.
+        if response_format is None:
+            response_format = "verbose_json"
+        if include_text is None:
+            include_text = True
+
+        # Prepare form data
+        data = {
+            "model": self.model,
+            "response_format": response_format,
+            "include_text": str(include_text).lower(),
+        }
+
+        if language is not None:
+            data["language"] = language
+        if num_speakers is not None:
+            data["num_speakers"] = str(num_speakers)
+        if min_speakers is not None:
+            data["min_speakers"] = str(min_speakers)
+        if max_speakers is not None:
+            data["max_speakers"] = str(max_speakers)
+        if clustering_threshold is not None:
+            data["clustering_threshold"] = str(clustering_threshold)
+        if min_duration_on is not None:
+            data["min_duration_on"] = str(min_duration_on)
+        if min_duration_off is not None:
+            data["min_duration_off"] = str(min_duration_off)
+
+        # Open file
+        if not os.path.exists(audio_path):
+            raise LocalAIError(f"Audio file not found: {audio_path}")
+
+        with open(audio_path, "rb") as f:
+            files = {
+                "file": (os.path.basename(audio_path), f, "application/octet-stream")
+            }
+
+            headers = {}
+            if self.api_key:
+                headers["Authorization"] = f"Bearer {self.api_key}"
+
+            # POST /v1/audio/diarization
+            resp = self._client.post(
+                "/v1/audio/diarization",
+                data=data,
+                files=files,
+                headers=headers,
+            )
+
+        if resp.status_code >= 400:
+            body = resp.text
+            raise LocalAIError(
+                f"LocalAI request failed with status {resp.status_code}: {body}"
+            )
+
+        try:
+            result = resp.json()
+        except json.JSONDecodeError:
+            raise LocalAIError(
+                "Failed to parse LocalAI response as JSON."
+            )
+
+        if verbose:
+            print("Diarization and transcription finished. Starting post-processing.")
+
+        return self._parse_diarization_response(result)
+
+    def _parse_diarization_response(self, result: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Convert LocalAI response into the internal format used by Scraibe:
+        {
+          "segments": [ [start, end], ... ],
+          "speakers": [ "SPEAKER_00", ... ],
+          "transcripts": [ "text for segment", ... ]
+        }
+        """
+        segments = result.get("segments", [])
+
+        if not segments:
+            # If no segments, return empty but valid structure
+            return {
+                "segments": [],
+                "speakers": [],
+                "transcripts": [],
+            }
+
+        out_segments = []
+        out_speakers = []
+        out_transcripts = []
+
+        for seg in segments:
+            start = float(seg.get("start", 0.0))
+            end = float(seg.get("end", 0.0))
+            speaker = seg.get("speaker", "SPEAKER_00")
+            text = seg.get("text", "").strip()
+
+            out_segments.append([start, end])
+            out_speakers.append(speaker)
+            out_transcripts.append(text)
+
+        return {
+            "segments": out_segments,
+            "speakers": out_speakers,
+            "transcripts": out_transcripts,
+        }

scraibe/misc.py

@@ -1,77 +1,52 @@
 import os
-import yaml
 from argparse import Action
 from ast import literal_eval
-from torch.cuda import is_available
-from torch import get_num_threads, set_num_threads
 
 CACHE_DIR = os.getenv(
     "AUTOT_CACHE",
     os.path.expanduser("~/.cache/torch/models"),
 )
-os.environ["PYANNOTE_CACHE"] = os.getenv(
-    "PYANNOTE_CACHE",
-    os.path.join(CACHE_DIR, "pyannote"),
-)
 
+# Legacy paths kept for backward compatibility (ignored by LocalAI client)
 WHISPER_DEFAULT_PATH = os.path.join(CACHE_DIR, "whisper")
 PYANNOTE_DEFAULT_PATH = os.path.join(CACHE_DIR, "pyannote")
-PYANNOTE_DEFAULT_CONFIG = os.path.join(PYANNOTE_DEFAULT_PATH, "config.yaml") \
-    if os.path.exists(os.path.join(PYANNOTE_DEFAULT_PATH, "config.yaml")) \
-    else ('Jaikinator/ScrAIbe', 'pyannote/speaker-diarization-3.1')
+PYANNOTE_DEFAULT_CONFIG = os.path.join(PYANNOTE_DEFAULT_PATH, "config.yaml")
 
-SCRAIBE_TORCH_DEVICE =  os.getenv("SCRAIBE_TORCH_DEVICE", "cuda" if is_available() else "cpu")
 
-SCRAIBE_NUM_THREADS = os.getenv("SCRAIBE_NUM_THREADS", min(8, get_num_threads()))
-
-def config_diarization_yaml(file_path: str, path_to_segmentation: str = None) -> None:
-    """Configure diarization pipeline from a YAML file.
-
-    This function updates the YAML file to use the given segmentation model
-    offline, and avoids manual file manipulation.
-
-    Args:
-        file_path (str): Path to the YAML file.
-        path_to_segmentation (str, optional): Optional path to the segmentation model.
-
-    Raises:
-        FileNotFoundError: If the segmentation model file is not found.
+def set_threads(parse_threads=None, yaml_threads=None):
     """
-    with open(file_path, "r") as stream:
-        yml = yaml.safe_load(stream)
+    Configure number of threads.
 
-    segmentation_path = path_to_segmentation or os.path.join(
-        PYANNOTE_DEFAULT_PATH, "pytorch_model.bin")
-    yml["pipeline"]["params"]["segmentation"] = segmentation_path
-
-    if not os.path.exists(segmentation_path):
-        raise FileNotFoundError(
-            f"Segmentation model not found at {segmentation_path}")
-
-    with open(file_path, "w") as stream:
-        yaml.dump(yml, stream)
-
-
-def set_threads(parse_threads=None,
-                yaml_threads=None):
-    global SCRAIBE_NUM_THREADS
+    In LocalAI mode, this is mainly kept for backward compatibility.
+    """
+    chosen = None
     if parse_threads is not None:
         if not isinstance(parse_threads, int):
-            # probably covered with int type of parser arg
-            raise ValueError(f"Type of --num-threads must be int, but the type is {type(parse_threads)}")
+            raise ValueError(
+                f"Type of --num-threads must be int, but the type is {type(parse_threads)}"
+            )
         elif parse_threads < 1:
-            raise ValueError(f"Number of threads must be a positive integer, {parse_threads} was given")
+            raise ValueError(
+                f"Number of threads must be a positive integer, {parse_threads} was given"
+            )
         else:
-            set_num_threads(parse_threads)
-            SCRAIBE_NUM_THREADS = parse_threads
+            chosen = parse_threads
     elif yaml_threads is not None:
         if not isinstance(yaml_threads, int):
-            raise ValueError(f"Type of num_threads must be int, but the type is {type(yaml_threads)}")
+            raise ValueError(
+                f"Type of num_threads must be int, but the type is {type(yaml_threads)}"
+            )
         elif yaml_threads < 1:
-            raise ValueError(f"Number of threads must be a positive integer, {yaml_threads} was given")
+            raise ValueError(
+                f"Number of threads must be a positive integer, {yaml_threads} was given"
+            )
         else:
-            set_num_threads(yaml_threads)
-            SCRAIBE_NUM_THREADS = yaml_threads
+            chosen = yaml_threads
+
+    if chosen is not None:
+        os.environ["OMP_NUM_THREADS"] = str(chosen)
+        os.environ["MKL_NUM_THREADS"] = str(chosen)
+
 
 class ParseKwargs(Action):
     """
@@ -81,7 +56,7 @@ class ParseKwargs(Action):
     def __call__(self, parser, namespace, values, option_string=None):
         setattr(namespace, self.dest, dict())
         for value in values:
-            key, value = value.split('=')
+            key, value = value.split("=")
             try:
                 value = literal_eval(value)
             except:

scraibe/summarizer.py

@@ -0,0 +1,212 @@
+"""
+Summarizer Module
+-----------------
+
+Provides a client to summarize long transcripts via an LLM endpoint.
+
+Behavior:
+- Chunks transcript into 10,240-character segments.
+- Generates a summary for each chunk.
+- Combines all chunk summaries and produces a final, detailed summary.
+
+Environment Variables:
+- SUMMARIZER_API_URL: (required) Base URL of the LLM API (e.g., http://localhost:8080)
+- SUMMARIZER_API_KEY: (optional) API key, if required
+- SUMMARIZER_MODEL:   (optional) Model name (e.g., llama-3.1-8b-instruct)
+"""
+
+import os
+import json
+from typing import Optional
+
+import httpx
+
+
+class SummarizerError(Exception):
+    """Raised when the summarization API call fails."""
+    pass
+
+
+class SummarizerClient:
+    """
+    HTTP client for an OpenAI-compatible chat completions endpoint.
+    Used to summarize long transcripts in chunks.
+    """
+
+    CHUNK_SIZE = 10_240  # characters per chunk
+
+    def __init__(
+        self,
+        api_url: Optional[str] = None,
+        api_key: Optional[str] = None,
+        model: Optional[str] = None,
+        timeout: float = 600.0,
+    ):
+        self.api_url = (api_url or os.getenv("SUMMARIZER_API_URL")).strip().rstrip("/")
+        self.api_key = api_key or os.getenv("SUMMARIZER_API_KEY") or None
+        self.model = model or os.getenv("SUMMARIZER_MODEL") or "llama-3.1-8b-instruct"
+        self.timeout = timeout
+
+        if not self.api_url:
+            raise SummarizerError(
+                "SUMMARIZER_API_URL is not set. "
+                "Provide the summarization LLM URL via environment or constructor."
+            )
+
+        self._client = httpx.Client(
+            base_url=self.api_url,
+            timeout=self.timeout,
+            follow_redirects=True,
+        )
+
+    def close(self):
+        self._client.close()
+
+    def __del__(self):
+        try:
+            self._client.close()
+        except Exception:
+            pass
+
+    def summarize_transcript(self, transcript: str) -> str:
+        """
+        Summarize a (possibly very long) transcript.
+
+        Strategy:
+        - Split transcript into chunks of CHUNK_SIZE characters.
+        - Generate a detailed summary for each chunk.
+        - Combine all chunk summaries and generate a final, concise but thorough summary.
+
+        The final summary should make it clear:
+        - What was discussed
+        - Main issues
+        - Outcomes / decisions
+        - Next steps / action items
+        """
+        if not transcript.strip():
+            return "No transcript provided to summarize."
+
+        # 1) Chunk the transcript
+        chunks = self._chunk_text(transcript)
+
+        # 2) Summarize each chunk
+        chunk_summaries = []
+        for i, chunk in enumerate(chunks):
+            summary = self._summarize_chunk(chunk, i, len(chunks))
+            chunk_summaries.append(summary)
+
+        # 3) Combine and summarize summaries
+        combined = "\n\n".join(chunk_summaries)
+        final_summary = self._summarize_combined(combined)
+
+        return final_summary
+
+    def _chunk_text(self, text: str) -> list[str]:
+        """Split text into chunks of CHUNK_SIZE characters."""
+        chunks = []
+        start = 0
+        while start < len(text):
+            end = start + self.CHUNK_SIZE
+            if end >= len(text):
+                chunks.append(text[start:])
+                break
+            # Try to break at a reasonable boundary (newline or space)
+            break_pos = text.rfind("\n", start, end)
+            if break_pos == -1:
+                break_pos = text.rfind(" ", start, end)
+            if break_pos == -1 or break_pos <= start:
+                break_pos = end
+            chunks.append(text[start:break_pos].strip())
+            start = break_pos
+        return chunks
+
+    def _summarize_chunk(self, chunk: str, index: int, total: int) -> str:
+        system_prompt = (
+            "You are an expert legal and business meeting summarizer. "
+            "You will receive a segment of a longer transcript. "
+            "Provide a detailed, structured summary of this segment, focusing on: "
+            "- Topics discussed\n"
+            "- Key points and arguments\n"
+            "- Decisions and agreements\n"
+            "- Action items and responsibilities\n"
+            "- Any risks, conflicts, or open issues\n\n"
+            "Be concise but complete. Use bullet points when helpful. "
+            "Do not add information that is not present in the transcript."
+        )
+
+        user_prompt = (
+            f"This is segment {index + 1} of {total} from a longer conversation.\n\n"
+            f"{chunk}"
+        )
+
+        return self._chat_completion(system_prompt, user_prompt)
+
+    def _summarize_combined(self, combined_summaries: str) -> str:
+        system_prompt = (
+            "You are an expert legal and business meeting summarizer. "
+            "You will receive several intermediate summaries of a longer conversation. "
+            "Produce a single, comprehensive summary that makes it clear: "
+            "- The overall purpose and context of the discussion\n"
+            "- The main issues and topics addressed\n"
+            "- Key arguments and positions (briefly)\n"
+            "- Decisions and outcomes\n"
+            "- Action items, responsibilities, and next steps\n"
+            "- Any unresolved issues or risks\n\n"
+            "The summary should be detailed enough that a reader who was not present "
+            "can understand what happened and what is expected going forward. "
+            "Use clear, concise language and bullet points where appropriate."
+        )
+
+        user_prompt = (
+            "Here are the intermediate summaries from different parts of the same conversation:\n\n"
+            f"{combined_summaries}"
+        )
+
+        return self._chat_completion(system_prompt, user_prompt)
+
+    def _chat_completion(self, system_prompt: str, user_prompt: str) -> str:
+        """
+        Call OpenAI-compatible /v1/chat/completions endpoint.
+        """
+        payload = {
+            "model": self.model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt},
+            ],
+            "temperature": 0.3,
+        }
+
+        headers = {
+            "Content-Type": "application/json",
+        }
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+
+        resp = self._client.post(
+            "/v1/chat/completions",
+            json=payload,
+            headers=headers,
+        )
+
+        if resp.status_code >= 400:
+            raise SummarizerError(
+                f"Summarizer API error {resp.status_code}: {resp.text}"
+            )
+
+        try:
+            data = resp.json()
+        except json.JSONDecodeError:
+            raise SummarizerError(
+                "Failed to parse summarizer response as JSON."
+            )
+
+        # Extract assistant message
+        try:
+            content = data["choices"][0]["message"]["content"]
+            return content.strip()
+        except (KeyError, IndexError, TypeError):
+            raise SummarizerError(
+                "Unexpected summarizer response format: "
+                f"{json.dumps(data, indent=2)}"
+            )