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<!DOCTYPE html>
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<title>autotranscript package &#8212; AutoTranscript documentation</title>
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<section id="autotranscript-package">
<h1>autotranscript package<a class="headerlink" href="#autotranscript-package" title="Permalink to this heading"></a></h1>
<section id="subpackages">
<h2>Subpackages<a class="headerlink" href="#subpackages" title="Permalink to this heading"></a></h2>
<div class="toctree-wrapper compound">
<ul>
<li class="toctree-l1"><a class="reference internal" href="autotranscript.app.html">autotranscript.app package</a><ul>
<li class="toctree-l2"><a class="reference internal" href="autotranscript.app.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="autotranscript.app.html#module-autotranscript.app.gradio_app">autotranscript.app.gradio_app module</a><ul>
<li class="toctree-l3"><a class="reference internal" href="autotranscript.app.html#gradio-audio-transcription-app">Gradio Audio Transcription App.</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="autotranscript.app.html#module-autotranscript.app.qtfaststart">autotranscript.app.qtfaststart module</a></li>
<li class="toctree-l2"><a class="reference internal" href="autotranscript.app.html#module-autotranscript.app">Module contents</a></li>
</ul>
</li>
</ul>
</div>
</section>
<section id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this heading"></a></h2>
</section>
<section id="module-autotranscript.audio">
<span id="autotranscript-audio-module"></span><h2>autotranscript.audio module<a class="headerlink" href="#module-autotranscript.audio" title="Permalink to this heading"></a></h2>
<section id="audio-processor-module">
<h3>Audio Processor Module<a class="headerlink" href="#audio-processor-module" title="Permalink to this heading"></a></h3>
<p>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.</p>
<p>Available Classes:
- AudioProcessor: Processes audio waveforms and provides methods for loading,</p>
<blockquote>
<div><p>cutting, and handling audio.</p>
</div></blockquote>
<dl>
<dt>Usage:</dt><dd><p>from .audio_import AudioProcessor</p>
<p>processor = AudioProcessor.from_file(“path/to/audiofile.wav”)
cut_waveform = processor.cut(start=1.0, end=5.0)</p>
</dd>
</dl>
<p>Constants:
- SAMPLE_RATE (int): Default sample rate for processing.
- NORMALIZATION_FACTOR (float): Normalization factor for audio waveform.</p>
<dl class="py class">
<dt class="sig sig-object py" id="autotranscript.audio.AudioProcessor">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">AudioProcessor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">waveform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sr</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">16000</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.audio.AudioProcessor" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Audio Processor class that leverages PyTorchaudio to provide functionalities
for loading, cutting, and handling audio waveforms.</p>
<dl class="simple">
<dt>Attributes:</dt><dd><dl class="simple">
<dt>waveform: torch.Tensor</dt><dd><p>The audio waveform tensor.</p>
</dd>
<dt>sr: int</dt><dd><p>The sample rate of the audio.</p>
</dd>
</dl>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.audio.AudioProcessor.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">waveform</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sr</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">16000</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.audio.AudioProcessor.__init__" title="Permalink to this definition"></a></dt>
<dd><p>Initialize the AudioProcessor object.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>waveform (torch.Tensor): The audio waveform tensor.
sr (int, optional): The sample rate of the audio. Defaults to SAMPLE_RATE.
args: Additional arguments.
kwargs: Additional keyword arguments, e.g., device to use for processing.
If CUDA is available, it defaults to CUDA.</p>
</dd>
<dt>Raises:</dt><dd><p>ValueError: If the provided sample rate is not of type int.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.audio.AudioProcessor.__repr__">
<span class="sig-name descname"><span class="pre">__repr__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="headerlink" href="#autotranscript.audio.AudioProcessor.__repr__" title="Permalink to this definition"></a></dt>
<dd><p>Return repr(self).</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.audio.AudioProcessor.cut">
<span class="sig-name descname"><span class="pre">cut</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="headerlink" href="#autotranscript.audio.AudioProcessor.cut" title="Permalink to this definition"></a></dt>
<dd><p>Cut a segment from the audio waveform between the specified start and end times.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>start (float): Start time in seconds.
end (float): End time in seconds.</p>
</dd>
<dt>Returns:</dt><dd><p>torch.Tensor: The cut waveform segment.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.audio.AudioProcessor.from_file">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#autotranscript.audio.AudioProcessor" title="autotranscript.audio.AudioProcessor"><span class="pre">AudioProcessor</span></a></span></span><a class="headerlink" href="#autotranscript.audio.AudioProcessor.from_file" title="Permalink to this definition"></a></dt>
<dd><p>Create an AudioProcessor instance from an audio file.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>file (str): The audio file path.</p>
</dd>
<dt>Returns:</dt><dd><p>AudioProcessor: An instance of the AudioProcessor class containing the loaded audio.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.audio.AudioProcessor.load_audio">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load_audio</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sr</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">16000</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.audio.AudioProcessor.load_audio" title="Permalink to this definition"></a></dt>
<dd><p>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.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>file (str): The audio file to open.
sr (int, optional): The desired sample rate. Defaults to SAMPLE_RATE.</p>
</dd>
<dt>Returns:</dt><dd><dl class="simple">
<dt>tuple: A NumPy array containing the audio waveform in float32 dtype</dt><dd><p>and the sample rate.</p>
</dd>
</dl>
</dd>
<dt>Raises:</dt><dd><p>RuntimeError: If failed to load audio.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
</section>
<section id="module-autotranscript.autotranscript">
<span id="autotranscript-autotranscript-module"></span><h2>autotranscript.autotranscript module<a class="headerlink" href="#module-autotranscript.autotranscript" title="Permalink to this heading"></a></h2>
<section id="autotranscribe-class">
<h3>AutoTranscribe Class<a class="headerlink" href="#autotranscribe-class" title="Permalink to this heading"></a></h3>
<p>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.</p>
<p>By encapsulating the complexities of underlying models, it allows for straightforward
integration into various applications, ranging from transcription services to voice assistants.</p>
<p>Available Classes:
- AutoTranscribe: Main class for performing transcription and diarization.</p>
<blockquote>
<div><p>Includes methods for loading models, processing audio files,
and formatting the transcription output.</p>
</div></blockquote>
<dl>
<dt>Usage:</dt><dd><p>from .autotranscribe import AutoTranscribe</p>
<p>model = AutoTranscribe(whisper_model=”path/to/whisper/model”, dia_model=”path/to/diarisation/model”)
transcript = model.transcribe(“path/to/audiofile.wav”)</p>
</dd>
</dl>
<dl class="py class">
<dt class="sig sig-object py" id="autotranscript.autotranscript.AutoTranscribe">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">AutoTranscribe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">whisper_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">whisper</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dia_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">DiarisationType</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.autotranscript.AutoTranscribe" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>AutoTranscribe 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.</p>
<dl class="simple">
<dt>Attributes:</dt><dd><p>transcriber (Transcriber): The transcriber object to handle transcription.
diariser (Diariser): The diariser object to handle diarization.</p>
</dd>
<dt>Methods:</dt><dd><p>__init__: Initializes the AutoTranscribe 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.</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.autotranscript.AutoTranscribe.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">whisper_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">whisper</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dia_model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">DiarisationType</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.autotranscript.AutoTranscribe.__init__" title="Permalink to this definition"></a></dt>
<dd><p>Initializes the AutoTranscribe class.</p>
<dl class="simple">
<dt>Args:</dt><dd><dl class="simple">
<dt>whisper_model (Union[bool, str, whisper], optional): </dt><dd><p>Path to whisper model or whisper model itself.</p>
</dd>
<dt>diarisation_model (Union[bool, str, DiarisationType], optional): </dt><dd><p>Path to pyannote diarization model or model itself.</p>
</dd>
<dt><a href="#id1"><span class="problematic" id="id2">**</span></a>kwargs: Additional keyword arguments for whisper</dt><dd><p>and pyannote diarization models.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.autotranscript.AutoTranscribe.__repr__">
<span class="sig-name descname"><span class="pre">__repr__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.autotranscript.AutoTranscribe.__repr__" title="Permalink to this definition"></a></dt>
<dd><p>Return repr(self).</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.autotranscript.AutoTranscribe.autotranscribe">
<span class="sig-name descname"><span class="pre">autotranscribe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">audio_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_original</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#autotranscript.transcript_exporter.Transcript" title="autotranscript.transcript_exporter.Transcript"><span class="pre">Transcript</span></a></span></span><a class="headerlink" href="#autotranscript.autotranscript.AutoTranscribe.autotranscribe" title="Permalink to this definition"></a></dt>
<dd><p>Transcribes an audio file using the whisper model and pyannote diarization model.</p>
<dl>
<dt>Args:</dt><dd><dl class="simple">
<dt>audio_file (Union[str, torch.Tensor, ndarray]): </dt><dd><p>Path to audio file or a tensor representing the audio.</p>
</dd>
<dt>remove_original (bool, optional): If True, the original audio file will</dt><dd><p>be removed after transcription.</p>
</dd>
</dl>
<p><a href="#id3"><span class="problematic" id="id4">*</span></a>args: Additional positional arguments for diarization and transcription.
<a href="#id5"><span class="problematic" id="id6">**</span></a>kwargs: Additional keyword arguments for diarization and transcription.</p>
</dd>
<dt>Returns:</dt><dd><dl class="simple">
<dt>Transcript: A Transcript object containing the transcription,</dt><dd><p>which can be exported to different formats.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.autotranscript.AutoTranscribe.diarization">
<span class="sig-name descname"><span class="pre">diarization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">audio_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">dict</span></span></span><a class="headerlink" href="#autotranscript.autotranscript.AutoTranscribe.diarization" title="Permalink to this definition"></a></dt>
<dd><p>Perform diarization on an audio file using the pyannote diarization model.</p>
<dl class="simple">
<dt>Args:</dt><dd><dl class="simple">
<dt>audio_file (Union[str, torch.Tensor, ndarray]):</dt><dd><p>The audio source which can either be a path to the audio file or a tensor representation.</p>
</dd>
<dt><a href="#id7"><span class="problematic" id="id8">**</span></a>kwargs: </dt><dd><p>Additional keyword arguments for diarization.</p>
</dd>
</dl>
</dd>
<dt>Returns:</dt><dd><dl class="simple">
<dt>dict: </dt><dd><p>A dictionary containing the results of the diarization process.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.autotranscript.AutoTranscribe.get_audio_file">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_audio_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">audio_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#autotranscript.audio.AudioProcessor" title="autotranscript.audio.AudioProcessor"><span class="pre">AudioProcessor</span></a></span></span><a class="headerlink" href="#autotranscript.autotranscript.AutoTranscribe.get_audio_file" title="Permalink to this definition"></a></dt>
<dd><p>Gets an audio file as TorchAudioProcessor.</p>
<dl>
<dt>Args:</dt><dd><dl class="simple">
<dt>audio_file (Union[str, torch.Tensor, ndarray]): Path to the audio file or </dt><dd><p>a tensor representing the audio.</p>
</dd>
</dl>
<p><a href="#id9"><span class="problematic" id="id10">*</span></a>args: Additional positional arguments.
<a href="#id11"><span class="problematic" id="id12">**</span></a>kwargs: Additional keyword arguments.</p>
</dd>
<dt>Returns:</dt><dd><dl class="simple">
<dt>AudioProcessor: An object containing the waveform and sample rate in</dt><dd><p>torch.Tensor format.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.autotranscript.AutoTranscribe.remove_audio_file">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">remove_audio_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">audio_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shred</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.autotranscript.AutoTranscribe.remove_audio_file" title="Permalink to this definition"></a></dt>
<dd><p>Removes the original audio file to avoid disk space issues or ensure data privacy.</p>
<dl>
<dt>Args:</dt><dd><p>audio_file_path (str): Path to the audio file.
shred (bool, optional): If True, the audio file will be shredded,</p>
<blockquote>
<div><p>not just removed.</p>
</div></blockquote>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.autotranscript.AutoTranscribe.transcribe">
<span class="sig-name descname"><span class="pre">transcribe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">audio_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.autotranscript.AutoTranscribe.transcribe" title="Permalink to this definition"></a></dt>
<dd><p>Transcribe the provided audio file.</p>
<dl class="simple">
<dt>Args:</dt><dd><dl class="simple">
<dt>audio_file (Union[str, torch.Tensor, ndarray]):</dt><dd><p>The audio source, which can either be a path or a tensor representation.</p>
</dd>
<dt><a href="#id13"><span class="problematic" id="id14">**</span></a>kwargs: </dt><dd><p>Additional keyword arguments for transcription.</p>
</dd>
</dl>
</dd>
<dt>Returns:</dt><dd><dl class="simple">
<dt>str:</dt><dd><p>The transcribed text from the audio source.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
</section>
<section id="module-autotranscript.cli">
<span id="autotranscript-cli-module"></span><h2>autotranscript.cli module<a class="headerlink" href="#module-autotranscript.cli" title="Permalink to this heading"></a></h2>
<p>Command-Line Interface (CLI) for the AutoTranscribe 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.</p>
<dl class="py function">
<dt class="sig sig-object py" id="autotranscript.cli.cli">
<span class="sig-name descname"><span class="pre">cli</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.cli.cli" title="Permalink to this definition"></a></dt>
<dd><p>Command-Line Interface (CLI) for the AutoTranscribe 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.</p>
<p>This function can be executed from the command line to perform transcription tasks, providing a
user-friendly way to access the AutoTranscribe class functionalities.</p>
</dd></dl>
</section>
<section id="module-autotranscript.diarisation">
<span id="autotranscript-diarisation-module"></span><h2>autotranscript.diarisation module<a class="headerlink" href="#module-autotranscript.diarisation" title="Permalink to this heading"></a></h2>
<section id="diarisation-class">
<h3>Diarisation Class<a class="headerlink" href="#diarisation-class" title="Permalink to this heading"></a></h3>
<p>This class serves as the heart of the speaker diarization system, responsible for identifying
and segmenting individual speakers from a given audio file. It leverages a pretrained model
from pyannote.audio, providing an accessible interface for audio processing tasks such as
speaker separation, and timestamping.</p>
<p>By encapsulating the complexities of the underlying model, it allows for straightforward
integration into various applications, ranging from transcription services to voice assistants.</p>
<p>Available Classes:
- Diariser: Main class for performing speaker diarization.</p>
<blockquote>
<div><p>Includes methods for loading models, processing audio files,
and formatting the diarization output.</p>
</div></blockquote>
<p>Constants:
- TOKEN_PATH (str): Path to the Pyannote token.
- PYANNOTE_DEFAULT_PATH (str): Default path to Pyannote models.
- PYANNOTE_DEFAULT_CONFIG (str): Default configuration for Pyannote models.</p>
<dl>
<dt>Usage:</dt><dd><p>from .diarisation import Diariser</p>
<p>model = Diariser.load_model(model=”path/to/model/config.yaml”)
diarisation_output = model.diarization(“path/to/audiofile.wav”)</p>
</dd>
</dl>
<dl class="py class">
<dt class="sig sig-object py" id="autotranscript.diarisation.Diariser">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Diariser</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.diarisation.Diariser" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Handles the diarization process of an audio file using a pretrained model
from pyannote.audio. Diarization is the task of determining “who spoke when.”</p>
<dl class="simple">
<dt>Args:</dt><dd><p>model: The pretrained model to use for diarization.</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.diarisation.Diariser.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.diarisation.Diariser.__init__" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.diarisation.Diariser.__repr__">
<span class="sig-name descname"><span class="pre">__repr__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.diarisation.Diariser.__repr__" title="Permalink to this definition"></a></dt>
<dd><p>Return repr(self).</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.diarisation.Diariser.diarization">
<span class="sig-name descname"><span class="pre">diarization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">audiofile</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">dict</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Annotation</span></span></span><a class="headerlink" href="#autotranscript.diarisation.Diariser.diarization" title="Permalink to this definition"></a></dt>
<dd><p>Perform speaker diarization on the provided audio file,
effectively separating different speakers
and providing a timestamp for each segment.</p>
<dl>
<dt>Args:</dt><dd><dl class="simple">
<dt>audiofile: The path to the audio file or a torch.Tensor</dt><dd><p>containing the audio data.</p>
</dd>
</dl>
<p>args: Additional arguments for the diarization model.
kwargs: Additional keyword arguments for the diarization model.</p>
</dd>
<dt>Returns:</dt><dd><dl class="simple">
<dt>dict: A dictionary containing speaker names,</dt><dd><p>segments, and other information related
to the diarization process.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.diarisation.Diariser.format_diarization_output">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">format_diarization_output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dia</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Annotation</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">dict</span></span></span><a class="headerlink" href="#autotranscript.diarisation.Diariser.format_diarization_output" title="Permalink to this definition"></a></dt>
<dd><p>Formats the raw diarization output into a more usable structure for this project.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>dia: Raw diarization output.</p>
</dd>
<dt>Returns:</dt><dd><dl class="simple">
<dt>dict: A structured representation of the diarization, with speaker names</dt><dd><p>as keys and a list of tuples representing segments as values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.diarisation.Diariser.load_model">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'/home/ortizcruzc/.cache/torch/models/pyannote/config.yaml'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_auth_token</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_token</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Path</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'/home/ortizcruzc/.cache/torch/models/pyannote'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hparams_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Path</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">pyannote.audio.Pipeline</span></span></span><a class="headerlink" href="#autotranscript.diarisation.Diariser.load_model" title="Permalink to this definition"></a></dt>
<dd><p>Loads a pretrained model from pyannote.audio,
either from a local cache or online repository.</p>
<dl>
<dt>Args:</dt><dd><dl class="simple">
<dt>model: Path or identifier for the pyannote model.</dt><dd><p>default: /models/pyannote/speaker_diarization/config.yaml</p>
</dd>
</dl>
<p>token: Optional HUGGINGFACE_TOKEN for authenticated access.
cache_token: Whether to cache the token locally for future use.
cache_dir: Directory for caching models.
hparams_file: Path to a YAML file containing hyperparameters.
args: Additional arguments only to avoid errors.
kwargs: Additional keyword arguments only to avoid errors.</p>
</dd>
<dt>Returns:</dt><dd><p>Pipeline: A pyannote.audio Pipeline object, encapsulating the loaded model.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
</section>
<section id="module-autotranscript.misc">
<span id="autotranscript-misc-module"></span><h2>autotranscript.misc module<a class="headerlink" href="#module-autotranscript.misc" title="Permalink to this heading"></a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="autotranscript.misc.config_diarization_yaml">
<span class="sig-name descname"><span class="pre">config_diarization_yaml</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path_to_segmentation</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.misc.config_diarization_yaml" title="Permalink to this definition"></a></dt>
<dd><p>Configure diarization pipeline from a YAML file.</p>
<p>This function updates the YAML file to use the given segmentation model
offline, and avoids manual file manipulation.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>file_path (str): Path to the YAML file.
path_to_segmentation (str, optional): Optional path to the segmentation model.</p>
</dd>
<dt>Raises:</dt><dd><p>FileNotFoundError: If the segmentation model file is not found.</p>
</dd>
</dl>
</dd></dl>
</section>
<section id="module-autotranscript.transcriber">
<span id="autotranscript-transcriber-module"></span><h2>autotranscript.transcriber module<a class="headerlink" href="#module-autotranscript.transcriber" title="Permalink to this heading"></a></h2>
<section id="transcriber-module">
<h3>Transcriber Module<a class="headerlink" href="#transcriber-module" title="Permalink to this heading"></a></h3>
<p>This module provides the Transcriber class, a comprehensive tool for working with Whisper models.
The Transcriber class offers functionalities such as loading different Whisper models, transcribing audio files,
and saving transcriptions to text files. It acts as an interface between various Whisper models and the user,
simplifying the process of audio transcription.</p>
<dl>
<dt>Main Features:</dt><dd><ul class="simple">
<li><p>Loading different sizes and versions of Whisper models.</p></li>
<li><p>Transcribing audio in various formats including str, Tensor, and nparray.</p></li>
<li><p>Saving the transcriptions to the specified paths.</p></li>
<li><p>Adaptable to various language specifications.</p></li>
<li><p>Options to control the verbosity of the transcription process.</p></li>
</ul>
</dd>
<dt>Constants:</dt><dd><p>WHISPER_DEFAULT_PATH: Default path for downloading and loading Whisper models.</p>
</dd>
<dt>Usage:</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">your_package</span> <span class="kn">import</span> <span class="n">Transcriber</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">transcriber</span> <span class="o">=</span> <span class="n">Transcriber</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">&quot;medium&quot;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">transcript</span> <span class="o">=</span> <span class="n">transcriber</span><span class="o">.</span><span class="n">transcribe</span><span class="p">(</span><span class="n">audio</span><span class="o">=</span><span class="s2">&quot;path/to/audio.wav&quot;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">transcriber</span><span class="o">.</span><span class="n">save_transcript</span><span class="p">(</span><span class="n">transcript</span><span class="p">,</span> <span class="s2">&quot;path/to/save.txt&quot;</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
<dl class="py class">
<dt class="sig sig-object py" id="autotranscript.transcriber.Transcriber">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Transcriber</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">whisper</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.transcriber.Transcriber" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>The Transcriber class serves as a wrapper around Whisper models for efficient audio
transcription. By encapsulating the intricacies of loading models, processing audio,
and saving transcripts, it offers an easy-to-use interface
for users to transcribe audio files.</p>
<dl>
<dt>Attributes:</dt><dd><p>model (whisper): The Whisper model used for transcription.</p>
</dd>
<dt>Methods:</dt><dd><p>transcribe: Transcribes the given audio file.
save_transcript: Saves the transcript to a file.
load_model: Loads a specific Whisper model.
_get_whisper_kwargs: Private method to get valid keyword arguments for the whisper model.</p>
</dd>
<dt>Examples:</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">transcriber</span> <span class="o">=</span> <span class="n">Transcriber</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">&quot;medium&quot;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">transcript</span> <span class="o">=</span> <span class="n">transcriber</span><span class="o">.</span><span class="n">transcribe</span><span class="p">(</span><span class="n">audio</span><span class="o">=</span><span class="s2">&quot;path/to/audio.wav&quot;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">transcriber</span><span class="o">.</span><span class="n">save_transcript</span><span class="p">(</span><span class="n">transcript</span><span class="p">,</span> <span class="s2">&quot;path/to/save.txt&quot;</span><span class="p">)</span>
</pre></div>
</div>
</dd>
<dt>Note:</dt><dd><p>The class supports various sizes and versions of Whisper models. Please refer to
the load_model method for available options.</p>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcriber.Transcriber.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">whisper</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.transcriber.Transcriber.__init__" title="Permalink to this definition"></a></dt>
<dd><p>Initialize the Transcriber class with a Whisper model.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>model (whisper): The Whisper model to use for transcription.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcriber.Transcriber.__repr__">
<span class="sig-name descname"><span class="pre">__repr__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="headerlink" href="#autotranscript.transcriber.Transcriber.__repr__" title="Permalink to this definition"></a></dt>
<dd><p>Return repr(self).</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcriber.Transcriber.load_model">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'medium'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_root</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'/home/ortizcruzc/.cache/torch/models/whisper'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.device</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_memory</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#autotranscript.transcriber.Transcriber" title="autotranscript.transcriber.Transcriber"><span class="pre">Transcriber</span></a></span></span><a class="headerlink" href="#autotranscript.transcriber.Transcriber.load_model" title="Permalink to this definition"></a></dt>
<dd><p>Load whisper model.</p>
<dl>
<dt>Args:</dt><dd><dl class="simple">
<dt>model (str): Whisper model. Available models include:</dt><dd><ul class="simple">
<li><p>‘tiny.en’</p></li>
<li><p>‘tiny’</p></li>
<li><p>‘base.en’</p></li>
<li><p>‘base’</p></li>
<li><p>‘small.en’</p></li>
<li><p>‘small’</p></li>
<li><p>‘medium.en’</p></li>
<li><p>‘medium’</p></li>
<li><p>‘large-v1’</p></li>
<li><p>‘large-v2’</p></li>
<li><p>‘large’</p></li>
</ul>
</dd>
<dt>download_root (str, optional): Path to download the model.</dt><dd><p>Defaults to WHISPER_DEFAULT_PATH.</p>
</dd>
<dt>device (Optional[Union[str, torch.device]], optional): </dt><dd><p>Device to load model on. Defaults to None.</p>
</dd>
<dt>in_memory (bool, optional): Whether to load model in memory. </dt><dd><p>Defaults to False.</p>
</dd>
</dl>
<p>args: Additional arguments only to avoid errors.
kwargs: Additional keyword arguments only to avoid errors.</p>
</dd>
<dt>Returns:</dt><dd><p>Transcriber: A Transcriber object initialized with the specified model.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcriber.Transcriber.save_transcript">
<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">save_transcript</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transcript</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.transcriber.Transcriber.save_transcript" title="Permalink to this definition"></a></dt>
<dd><p>Save a transcript to a file.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>transcript (str): The transcript as a string.
save_path (str): The path to save the transcript.</p>
</dd>
<dt>Returns:</dt><dd><p>None</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcriber.Transcriber.transcribe">
<span class="sig-name descname"><span class="pre">transcribe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">audio</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="headerlink" href="#autotranscript.transcriber.Transcriber.transcribe" title="Permalink to this definition"></a></dt>
<dd><p>Transcribe an audio file.</p>
<dl>
<dt>Args:</dt><dd><p>audio (Union[str, Tensor, nparray]): The audio file to transcribe.
<a href="#id15"><span class="problematic" id="id16">*</span></a>args: Additional arguments.
<a href="#id17"><span class="problematic" id="id18">**</span></a>kwargs: Additional keyword arguments,</p>
<blockquote>
<div><p>such as the language of the audio file.</p>
</div></blockquote>
</dd>
<dt>Returns:</dt><dd><p>str: The transcript as a string.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
</section>
<section id="module-autotranscript.transcript_exporter">
<span id="autotranscript-transcript-exporter-module"></span><h2>autotranscript.transcript_exporter module<a class="headerlink" href="#module-autotranscript.transcript_exporter" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Transcript</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transcript</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Class for storing transcript data, including speaker information and text segments,
and exporting it to various file formats such as JSON, HTML, and LaTeX.</p>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">transcript</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.__init__" title="Permalink to this definition"></a></dt>
<dd><p>Initializes the Transcript object with the given transcript data.</p>
<dl class="simple">
<dt>Args:</dt><dd><dl class="simple">
<dt>transcript (dict): A dictionary containing the formatted transcript string.</dt><dd><p>Keys should correspond to segment IDs, and values should
contain speaker and segment information.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.__repr__">
<span class="sig-name descname"><span class="pre">__repr__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.__repr__" title="Permalink to this definition"></a></dt>
<dd><p>Return a string representation of the Transcript object.</p>
<dl class="simple">
<dt>Returns:</dt><dd><p>str: A string that provides an informative description of the object.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.__str__">
<span class="sig-name descname"><span class="pre">__str__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.__str__" title="Permalink to this definition"></a></dt>
<dd><p>Converts the transcript to a string representation.</p>
<dl class="simple">
<dt>Returns:</dt><dd><dl class="simple">
<dt>str: String representation of the transcript, including speaker names and</dt><dd><p>time stamps for each segment.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.annotate">
<span class="sig-name descname"><span class="pre">annotate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">dict</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.annotate" title="Permalink to this definition"></a></dt>
<dd><p>Annotates the transcript to associate specific names with speakers.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>args (list): List of speaker names. These will be mapped sequentially to the speakers.
kwargs (dict): Dictionary with speaker names as keys and list of segments as values.</p>
</dd>
<dt>Returns:</dt><dd><p>dict: Dictionary with speaker names as keys and list of segments as values.</p>
</dd>
<dt>Raises:</dt><dd><dl class="simple">
<dt>ValueError: If the number of speaker names does not match the number </dt><dd><p>of speakers, or if an unknown speaker is found.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.from_json">
<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">json</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#autotranscript.transcript_exporter.Transcript" title="autotranscript.transcript_exporter.Transcript"><span class="pre">Transcript</span></a></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.from_json" title="Permalink to this definition"></a></dt>
<dd><p>Load transcript from json file</p>
<dl class="simple">
<dt>Args:</dt><dd><p>path (str): path to json file</p>
</dd>
<dt>Returns:</dt><dd><p>Transcript: Transcript object</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.get_dict">
<span class="sig-name descname"><span class="pre">get_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">dict</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.get_dict" title="Permalink to this definition"></a></dt>
<dd><p>Get transcript as dict</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>transcript as dict</p>
</dd>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>dict</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.get_html">
<span class="sig-name descname"><span class="pre">get_html</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.get_html" title="Permalink to this definition"></a></dt>
<dd><p>Get transcript as html string</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>transcript as html string</p>
</dd>
<dt class="field-even">Return type<span class="colon">:</span></dt>
<dd class="field-even"><p>str</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.get_json">
<span class="sig-name descname"><span class="pre">get_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_annotation</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.get_json" title="Permalink to this definition"></a></dt>
<dd><p>Get transcript as json string
:return: transcript as json string
:rtype: str</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.get_md">
<span class="sig-name descname"><span class="pre">get_md</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.get_md" title="Permalink to this definition"></a></dt>
<dd><p>Get transcript as Markdown string, using HTML formatting.</p>
<dl class="simple">
<dt>Returns:</dt><dd><p>str: Transcript as a Markdown string.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.get_tex">
<span class="sig-name descname"><span class="pre">get_tex</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.get_tex" title="Permalink to this definition"></a></dt>
<dd><p>Get transcript as LaTeX string. If no annotations are present, the speakers will
be annotated with the first letters of the alphabet.</p>
<dl class="simple">
<dt>Returns:</dt><dd><p>str: Transcript as LaTeX string.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.save">
<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.save" title="Permalink to this definition"></a></dt>
<dd><p>Save transcript to file with the given path and file format.</p>
<p>This method can save the transcript in various formats including JSON, TXT,
MD, HTML, TEX, and PDF. The file format is determined by the extension of
the path.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>path (str): Path to save the file, including the desired file extension.
<a href="#id19"><span class="problematic" id="id20">*</span></a>args: Additional positional arguments to be passed to the specific save methods.
<a href="#id21"><span class="problematic" id="id22">**</span></a>kwargs: Additional keyword arguments to be passed to the specific save methods.</p>
</dd>
<dt>Raises:</dt><dd><p>ValueError: If the file format specified in the path is unknown.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.to_html">
<span class="sig-name descname"><span class="pre">to_html</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.to_html" title="Permalink to this definition"></a></dt>
<dd><p>Save transcript as html file</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>path</strong> (<em>str</em>) – path to save file</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.to_json">
<span class="sig-name descname"><span class="pre">to_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.to_json" title="Permalink to this definition"></a></dt>
<dd><p>Save transcript as json file</p>
<dl class="simple">
<dt>Args:</dt><dd><p>path (str): path to save file</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.to_md">
<span class="sig-name descname"><span class="pre">to_md</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.to_md" title="Permalink to this definition"></a></dt>
<dd><p>Get transcript as Markdown string, using HTML formatting.</p>
<dl class="simple">
<dt>Returns:</dt><dd><p>str: Transcript as a Markdown string.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.to_pdf">
<span class="sig-name descname"><span class="pre">to_pdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.to_pdf" title="Permalink to this definition"></a></dt>
<dd><p>Save transcript as a PDF file (placeholder function, implementation needed).</p>
<dl class="simple">
<dt>Args:</dt><dd><p>path (str): Path to save the PDF file.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.to_tex">
<span class="sig-name descname"><span class="pre">to_tex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.to_tex" title="Permalink to this definition"></a></dt>
<dd><p>Save transcript as a LaTeX file (placeholder function, implementation needed).</p>
<dl class="simple">
<dt>Args:</dt><dd><p>path (str): Path to save the LaTeX file.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="autotranscript.transcript_exporter.Transcript.to_txt">
<span class="sig-name descname"><span class="pre">to_txt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="headerlink" href="#autotranscript.transcript_exporter.Transcript.to_txt" title="Permalink to this definition"></a></dt>
<dd><p>Save transcript as a LaTeX file (placeholder function, implementation needed).</p>
<dl class="simple">
<dt>Args:</dt><dd><p>path (str): Path to save the LaTeX file.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
<section id="module-autotranscript.version">
<span id="autotranscript-version-module"></span><h2>autotranscript.version module<a class="headerlink" href="#module-autotranscript.version" title="Permalink to this heading"></a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="autotranscript.version.get_version">
<span class="sig-name descname"><span class="pre">get_version</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">build_version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.version.get_version" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="autotranscript.version.git_version">
<span class="sig-name descname"><span class="pre">git_version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#autotranscript.version.git_version" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</section>
<section id="module-autotranscript">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-autotranscript" title="Permalink to this heading"></a></h2>
</section>
</section>
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