Merge pull request #37 from JSchmie/develop_gradio_app

Develop gradio app

.dockerignore

@@ -0,0 +1,6 @@
+scraibe/*__pycache__
+scraibe/app/*__pycache__
+scraibe/.pyannotetoken
+.git
+.gitignore
+.github

.gitignore

@@ -0,0 +1,6 @@
+transcibe.py
+scraibe/*__pycache__
+scraibe/app/*__pycache__
+scraibe/.pyannotetoken
+
+

scraibe/__init__.py

@@ -7,9 +7,6 @@ from .diarisation import *
 from .version import get_version as _get_version
 from .misc import *
 
-from .app.gradio_app import *
-from .app.qtfaststart import *
-
 from .cli import *
  
 __version__ = _get_version()

scraibe/app/__init__.py

@@ -1,2 +0,0 @@
-from .qtfaststart import *
-from .gradio_app import *

scraibe/app/gradio_app.py

@@ -1,441 +0,0 @@
-"""
-Gradio Audio Transcription App.
---------------------------------
-
-This module provides an interface to transcribe audio files using the 
-Scraibe model. Users can either upload an audio file or record their speech 
-live for transcription. The application supports multiple languages and provides 
-options to specify the number of speakers and the language of the audio.
-
-Attributes:
-    LANGUAGES (list): A list of supported languages for transcription.
-
-Usage:
-    Run this script to start the Gradio web interface for audio transcription.
-    
-"""
-
-"""
-Gradio Audio Transcription App.
---------------------------------
-
-This module provides an interface to transcribe audio files using the 
-Scraibe model. Users can either upload an audio file or record their speech 
-live for transcription. The application supports multiple languages and provides 
-options to specify the number of speakers and the language of the audio.
-
-Attributes:
-    LANGUAGES (list): A list of supported languages for transcription.
-
-Usage:
-    Run this script to start the Gradio web interface for audio transcription.
-    
-"""
-
-import json
-import os
-
-import gradio as gr
-from tqdm import tqdm
-
-from scraibe import Scraibe, Transcript
-
-theme = gr.themes.Soft(
-    primary_hue="green",
-    secondary_hue='orange',
-    neutral_hue="gray",  
-)
-
-LANGUAGES = [
-    "Afrikaans", "Arabic", "Armenian", "Azerbaijani", "Belarusian",
-    "Bosnian", "Bulgarian", "Catalan", "Chinese", "Croatian",
-    "Czech", "Danish", "Dutch", "English", "Estonian",
-    "Finnish", "French", "Galician", "German", "Greek",
-    "Hebrew", "Hindi", "Hungarian", "Icelandic", "Indonesian",
-    "Italian", "Japanese", "Kannada", "Kazakh", "Korean",
-    "Latvian", "Lithuanian", "Macedonian", "Malay", "Marathi",
-    "Maori", "Nepali", "Norwegian", "Persian", "Polish",
-    "Portuguese", "Romanian", "Russian", "Serbian", "Slovak",
-    "Slovenian", "Spanish", "Swahili", "Swedish", "Tagalog",
-    "Tamil", "Thai", "Turkish", "Ukrainian", "Urdu",
-    "Vietnamese", "Welsh"
-]
-
-CURRENT_PATH = os.path.dirname(os.path.realpath(__file__))
-
-class GradioTranscriptionInterface:
-    """
-    Interface handling the interaction between Gradio UI and the Audio Transcription system.
-    """
-
-    def __init__(self, model: Scraibe):
-        """
-        Initializes the GradioTranscriptionInterface with a transcription model.
-
-        Args:
-            model (Scraibe): Model responsible for audio transcription tasks.
-        """
-        self.model = model
-
-    def auto_transcribe(self, source,
-                        num_speakers : int,
-                        translation : bool,
-                        language : str):
-        """
-        Shortcut method for the Scraibe task.
-
-        Returns:
-            tuple: Transcribed text (str), JSON output (dict)
-        """
-        
-        kwargs = {
-            "num_speakers": num_speakers if num_speakers != 0 else None,
-            "language": language if language != "None" else None,
-            "task": 'translate' if translation else None
-        }
-        if isinstance(source, str):
-            try:
-                result = self.model.autotranscribe(source, **kwargs)
-            except ValueError:
-                raise gr.Error("Couldn't detect any speech in the provided audio. \
-                        Please try again!")
-            
-            return str(result), result.get_json()
-        
-        elif isinstance(source, list):
-            source_names = [s.split("/")[-1] for s in source]
-            result = []
-            for s in tqdm(source, total=len(source),desc = "Transcribing audio files"):
-                try:
-                    res = self.model.autotranscribe(s, **kwargs)
-                except ValueError:
-                    _name = s.split("/")[-1]
-                    res = f"NO TRANSCRIPT FOUND FOR {_name}"
-                    gr.Warning(f"Couldn't detect any speech in {_name} will skip this file.")
-                result.append(res)
-            
-            out = ''
-            out_dict = {}
-            for i, r in enumerate(result):
-                out += f"TRANSCRIPT FOR {source_names[i]}:\n\n"
-                out += str(r)
-                out += "\n\n"
-                
-                if isinstance(r, str):
-                    out_dict[source_names[i]] = r
-                else:
-                    out_dict[source_names[i]] = r.get_dict()
-              
-            return out, json.dumps(out_dict, indent=4)
-        
-        else:
-            raise gr.Error("Please provide a valid audio file.")
-
-
-    def transcribe(self, source, translation, language):
-        """
-        Shortcut method for the Transcribe task.
-
-        Returns:
-            str: Transcribed text.
-        """
-        kwargs = {
-            "language": language if language != "None" else None,
-            "task": 'translate' if translation == "Yes" else None
-        }
-        
-        if isinstance(source, str):
-            result = self.model.transcribe(source, **kwargs)
-            
-            return str(result)
-        
-        elif isinstance(source, list):
-            source_names = [s.split("/")[-1] for s in source]
-            result = []
-            for s in tqdm(source, total=len(source),desc = "Transcribing audio files"):
-                res = self.model.transcribe(s, **kwargs)
-                result.append(res)
-            
-            out = ''
-            for i, res in enumerate(result):
-                out += f"TRANSCRIPT FOR {source_names[i]}:\n\n"
-                out += str(res)
-                out += "\n\n"
-            
-            return out
-        
-        else:
-            raise gr.Error("Please provide a valid audio file.")
-
-    def perform_diarisation(self, source, num_speakers):
-        """
-        Shortcut method for the Diarisation task.
-
-        Returns:
-            str: JSON output of diarisation result.
-        """
-        kwargs = {
-            "num_speakers": num_speakers if num_speakers != 0 else None,
-        }
-        
-        if isinstance(source, str):
-            try:
-                result = self.model.diarization(source, **kwargs)
-            except ValueError:
-                raise gr.Error("Couldn't detect any speech in the provided audio. \
-                        Please try again!")
-        
-            return json.dumps(result, indent=2)
-        elif isinstance(source, list):
-            source_names = [s.split("/")[-1] for s in source]
-            result = []
-            for s in tqdm(source, total=len(source),desc = "Performing diarisation"):
-                try:
-                    res = self.model.diarization(s, **kwargs)
-                except ValueError:
-                    res = f"NO DIARISATION FOUND FOR {s}"
-                    gr.Warning(f"Couldn't detect any speech in {s} will skip this file.")
-                result.append(res)
-            
-            out = {}
-            
-            for i, res in enumerate(result):
-                out[source_names[i]] = res
-                
-            return json.dumps(out, indent=4)
-        
-        else:
-            gr.Error("Please provide a valid audio file.")
-            
-
-####
-# Gradio Interface
-####
-
-def gradio_Interface(model : Scraibe = None):
-    
-    if model is None:
-        model = Scraibe()
-        
-    pipe = GradioTranscriptionInterface(model)
-
-    def select_task(choice):
-        if choice == 'Auto Transcribe':
-            
-            return (gr.update(visible = True),
-                    gr.update(visible = True),
-                    gr.update(visible = True))
-                    
-            
-        elif choice == 'Transcribe':
-            
-            return (gr.update(visible = False),
-                    gr.update(visible = True),
-                    gr.update(visible = True))
-
-            
-        elif choice == 'Diarisation':
-            
-            return (gr.update(visible = True),
-                    gr.update(visible = False),
-                    gr.update(visible = False))
-        
-    def select_origin(choice):
-        if choice == "Upload Audio":
-            
-            return (gr.update(visible = True),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None))
-        
-        elif choice == "Record Audio":
-            
-            return (gr.update(visible = False, value = None),
-                    gr.update(visible = True),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None))
-
-        elif choice == "Upload Video":
-            
-            return (gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = True),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None))
-        
-        elif choice == "Record Video":
-            
-            return (gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = True),
-                    gr.update(visible = False, value = None))
-            
-        elif choice == "File or Files":
-            
-            return (gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = False, value = None),
-                    gr.update(visible = True))
-
-    def run_scribe(task,
-                   num_speakers,
-                   translate,
-                   language,
-                   audio1,
-                   audio2,
-                   video1,
-                   video2,
-                   file_in,
-                   progress = gr.Progress(track_tqdm= True)):
-        # get *args which are not None
-        progress(0, desc='Starting task...')
-        source = audio1 or audio2 or video1 or video2 or file_in
-        
-        if isinstance(source, list):
-            source = [s.name for s in source]
-            if len(source) == 1:
-                source = source[0]
- 
-        if task == 'Auto Transcribe':
-    
-            out_str , out_json = pipe.auto_transcribe(source = source,
-                                num_speakers = num_speakers,
-                                translation = translate,
-                                language = language)
-            
-            if isinstance(source, str):
-                return (gr.update(value = out_str, visible = True),
-                        gr.update(value = out_json, visible = True),
-                        gr.update(visible = True),
-                        gr.update(visible = True))      
-            else:
-                return (gr.update(value = out_str, visible = True),
-                        gr.update(value = out_json, visible = True),
-                        gr.update(visible = False),
-                        gr.update(visible = False))  
-            
-        elif task == 'Transcribe':
-            
-            out = pipe.transcribe(source = source,
-                                translation = translate,
-                                language = language)
-            
-            return (gr.update(value = out, visible = True),
-                    gr.update(value = None, visible = False),
-                    gr.update(visible = False),
-                    gr.update(visible = False))
-            
-        elif task == 'Diarisation':
-            
-            out = pipe.perform_diarisation(source = source,
-                                num_speakers = num_speakers)
-            
-            return (gr.update(value = None, visible = False),
-                    gr.update(value = out, visible = True),
-                    gr.update(visible = False),
-                    gr.update(visible = False))
-        
-    def annotate_output(annoation : str, out_json : dict):
-        # get *args which are not None
-        
-        trans = Transcript.from_json(out_json)
-        trans = trans.annotate(*annoation.split(","))
-
-        return gr.update(value = str(trans)),gr.update(value = trans.get_json())
-        
-        
-    with gr.Blocks(theme=theme,title='ScrAIbe: Automatic Audio Transcription') as demo:
-            
-        # Define components
-        hname = os.path.join(CURRENT_PATH, "header.html")
-        header = open(hname, "r").read()
-        
-        # ugly hack to get the logo to work
-        header = header.replace("/file=logo.svg", f"/file={CURRENT_PATH}/logo.svg" )
-        
-        gr.HTML(header, visible= True, show_label=False)
-        
-        with gr.Row():
-            
-            with gr.Column():
-            
-                task = gr.Radio(["Auto Transcribe", "Transcribe", "Diarisation"], label="Task",
-                                value= 'Auto Transcribe')
-                
-                num_speakers = gr.Number(value=0, label= "Number of speakers (optional)", 
-                                info = "Number of speakers in the audio file. If you don't know,\
-                                    leave it at 0.", visible= True)
-                
-                translate = gr.Checkbox(label="Translation", choices=[True, False], value = False,
-                                info="Select 'Yes' to have the output translated into English.",
-                                visible= True)
-                
-                language = gr.Dropdown(LANGUAGES,
-                                label="Language (optional)", value = "None",
-                                info="Language of the audio file. If you don't know,\
-                                    leave it at None.", visible= True)
-                
-                input = gr.Radio(["Upload Audio", "Record Audio", "Upload Video","Record Video" 
-                                    ,"File or Files"], label="Input Type", value="Upload Audio")
-                
-                audio1 = gr.Audio(source="upload", type="filepath", label="Upload Audio",
-                                    interactive= True, visible= True)
-                audio2 = gr.Audio(source="microphone", label="Record Audio", type="filepath",
-                                    interactive= True, visible= False)
-                video1 = gr.Video(source="upload", type="filepath", label="Upload Video",
-                                    interactive= True, visible= False)
-                video2 = gr.Video(source="webcam", label="Record Video", type="filepath",include_audio= True,
-                                    interactive= True, visible= False)
-                file_in = gr.Files(label="Upload File or Files", interactive= True, visible= False)
-                
-                submit = gr.Button()
-            
-            with gr.Column():
-                
-                out_txt = gr.Textbox(label="Output",
-                                        visible= True, show_copy_button=True)
-                
-                out_json = gr.JSON(label="JSON Output",
-                                    visible= False, show_copy_button=True)
-                
-                annoation = gr.Textbox(label="Name your speaker's",
-                                    info= "Please provide a list of the speakers arranged \
-                                    in the order in which they appear in the input. Use comma ',' \
-                                    as a seperator. Be aware that the first name is given \
-                                        to SPEAKER_00 the second to SPEAKER_01 and so on.",
-                                    visible= False, interactive= True)
-                
-                annotate = gr.Button(value="Annotate", visible= False, interactive= True)
-            
-        # Define usage of components
-        input.change(fn=select_origin, inputs=[input],
-                        outputs=[audio1, audio2, video1, video2, file_in])
-        
-        task.change(fn=select_task, inputs=[task],
-                    outputs=[num_speakers, translate, language])
-        
-        translate.change(fn= lambda x : gr.update(value = x),
-                            inputs=[translate], outputs=[translate])
-        num_speakers.change(fn= lambda x : gr.update(value = x),
-                            inputs=[num_speakers], outputs=[num_speakers])
-        language.change(fn= lambda x : gr.update(value = x), 
-                        inputs=[language], outputs=[language])
-        
-        submit.click(fn = run_scribe, 
-                        inputs=[task, num_speakers, translate, language, audio1,
-                                audio2, video1, video2, file_in],
-                        outputs=[out_txt, out_json, annoation, annotate])
-        
-        annotate.click(fn = annotate_output, inputs=[annoation, out_json],
-                        outputs=[out_txt, out_json])
-        
-    return demo
-
-    
-if __name__ == "__main__":
-    
-    gradio_Interface().queue().launch()

scraibe/app/header.html

@@ -1,66 +0,0 @@
-<!-- Importing Cormorant Garamond font from Google Fonts -->
-<link href="https://fonts.googleapis.com/css2?family=Cormorant+Garamond:wght@400;700&display=swap" rel="stylesheet">
-
-<style>
-    .header-container {
-        display: flex;
-        align-items: center;
-        justify-content: center;
-        position: relative;
-        padding-top: 30px;
-    }
-    .logo-container {
-        position: absolute;
-        top: 50%;
-        right: 20px;
-        transform: translateY(-50%);
-        width: 300px;
-    }
-    .logo {
-        width: 100%;
-        height: auto;
-    }
-    h1 {
-        font-family: 'Cormorant Garamond', serif;
-        font-size: 50px !important; /* Increased font size */
-        font-weight: bold;
-        color: #50AF31;
-        margin: 0;
-        position: relative;
-        padding: 0.5em 0;
-    }
-    h1::before, h1::after {
-        content: "";
-        position: absolute;
-        height: 2px;
-        width: 80%;
-        background-color: #50AF31;
-        left: 10%;
-    }
-    h1::before {
-        top: 0.5em;
-    }
-    h1::after {
-        bottom: 0.5em;
-    }
-    p, h2 {
-        font-size: 16px;
-        margin: 10px 0;
-        line-height: 1.4;
-    }
-</style>
-
-<div class="header-container">
-    <h1>ScrAIbe</h1>
-    <div class="logo-container">
-        <a href="https://www.kida-bmel.de/"> <!-- Replace with your actual URL -->
-            <img src="/file=logo.svg" alt="KIDA Logo" class="logo">
-        </a>
-    </div>
-</div>
-<div style="text-align: center; padding: 20px 10%;">
-    <p>
-        Upload, record, or provide a video with audio for transcription. Our toolkit is designed to transcribe content from multiple languages accurately. The integrated speaker diarisation feature identifies different speakers, ensuring a smooth transcription experience. For optimal results, indicate the number of speakers and the original language of the content.
-    </p>
-    <h2 style="font-weight: bold; color: #50AF31;">What would you like to do next?</h2>
-</div>

scraibe/app/qtfaststart.py

@@ -1,319 +0,0 @@
-"""
-This file contains a modified version of qtfaststart by qtfaststart
-https://github.com/danielgtaylor/qtfaststart/tree/master
-
-All credit goes to the original author.
-Copyright (C) 2008 - 2013 Daniel G. Taylor <dan@programmer-art.org>
-Permission is hereby granted, free of charge, to any person obtaining a copy of this
-software and associated documentation files (the "Software"),
-to deal in the Software without restriction, including without limitation the rights to 
-use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the 
-Software, and to permit persons to whom the Software is furnished to do so, 
-subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all copies
-or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
-INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
-DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
-ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS 
-IN THE SOFTWARE.
-"""
-
-import logging
-import os
-import struct
-import collections
-import io
-
-# define error classes
-class FastStartException(Exception):
-    """
-    Raised when something bad happens during processing.
-    """
-    pass
-
-class FastStartSetupError(FastStartException):
-    """
-    Rasised when asked to process a file that does not need processing
-    """
-    pass
-
-class MalformedFileError(FastStartException):
-    """
-    Raised when the input file is setup in an unexpected way
-    """
-    pass
-
-class UnsupportedFormatError(FastStartException):
-    """
-    Raised when a movie file is recognized as a format not supported.
-    """
-    pass
-
-# define constants
-CHUNK_SIZE = 8192
-
-log = logging.getLogger("qtfaststart")
-
-# Older versions of Python require this to be defined
-if not hasattr(os, 'SEEK_CUR'):
-    os.SEEK_CUR = 1
-
-Atom = collections.namedtuple('Atom', 'name position size')
-
-def read_atom(datastream):
-    """
-        Read an atom and return a tuple of (size, type) where size is the size
-        in bytes (including the 8 bytes already read) and type is a "fourcc"
-        like "ftyp" or "moov".
-    """
-    size, type = struct.unpack(">L4s", datastream.read(8))
-    type = type.decode('ascii')
-    return size, type
-
-
-def _read_atom_ex(datastream):
-    """
-    Read an Atom from datastream
-    """
-    pos = datastream.tell()
-    atom_size, atom_type = read_atom(datastream)
-    if atom_size == 1:
-        atom_size, = struct.unpack(">Q", datastream.read(8))
-    return Atom(atom_type, pos, atom_size)
-
-
-def get_index(datastream):
-    """
-        Return an index of top level atoms, their absolute byte-position in the
-        file and their size in a list:
-
-        index = [
-            ("ftyp", 0, 24),
-            ("moov", 25, 2658),
-            ("free", 2683, 8),
-            ...
-        ]
-
-        The tuple elements will be in the order that they appear in the file.
-    """
-    log.debug("Getting index of top level atoms...")
-
-    index = list(_read_atoms(datastream))
-    _ensure_valid_index(index)
-
-    return index
-
-
-def _read_atoms(datastream):
-    """
-    Read atoms until an error occurs
-    """
-    while datastream:
-        try:
-            atom = _read_atom_ex(datastream)
-            log.debug("%s: %s" % (atom.name, atom.size))
-        except:
-            break
-
-        yield atom
-
-        if atom.size == 0:
-            if atom.name == "mdat":
-                # Some files may end in mdat with no size set, which generally
-                # means to seek to the end of the file. We can just stop indexing
-                # as no more entries will be found!
-                break
-            else:
-                # Weird, but just continue to try to find more atoms
-                continue
-
-        datastream.seek(atom.position + atom.size)
-
-
-def _ensure_valid_index(index):
-    """
-    Ensure the minimum viable atoms are present in the index.
-
-    Raise FastStartException if not.
-    """
-    top_level_atoms = set([item.name for item in index])
-    for key in ["moov", "mdat"]:
-        if key not in top_level_atoms:
-            log.error("%s atom not found, is this a valid MOV/MP4 file?" % key)
-            raise FastStartException()
-
-
-def find_atoms(size, datastream):
-    """
-    Compatibilty interface for _find_atoms_ex
-    """
-    fake_parent = Atom('fake', datastream.tell()-8, size+8)
-    for atom in _find_atoms_ex(fake_parent, datastream):
-        yield atom.name
-
-
-def _find_atoms_ex(parent_atom, datastream):
-    """
-        Yield either "stco" or "co64" Atoms from datastream.
-        datastream will be 8 bytes into the stco or co64 atom when the value
-        is yielded.
-
-        It is assumed that datastream will be at the end of the atom after
-        the value has been yielded and processed.
-
-        parent_atom is the parent atom, a 'moov' or other ancestor of CO
-        atoms in the datastream.
-    """
-    stop = parent_atom.position + parent_atom.size
-
-    while datastream.tell() < stop:
-        try:
-            atom = _read_atom_ex(datastream)
-        except:
-            log.exception("Error reading next atom!")
-            raise FastStartException()
-
-        if atom.name in ["trak", "mdia", "minf", "stbl"]:
-            # Known ancestor atom of stco or co64, search within it!
-            for res in _find_atoms_ex(atom, datastream):
-                yield res
-        elif atom.name in ["stco", "co64"]:
-            yield atom
-        else:
-            # Ignore this atom, seek to the end of it.
-            datastream.seek(atom.position + atom.size)
-
-
-def process(infilename, limit=float('inf')):
-    """
-        Convert a Quicktime/MP4 file for streaming by moving the metadata to
-        the front of the file. This method writes a new file.
-
-        If limit is set to something other than zero it will be used as the
-        number of bytes to write of the atoms following the moov atom. This
-        is very useful to create a small sample of a file with full headers,
-        which can then be used in bug reports and such.
-    """
-    if isinstance(infilename, str):
-        datastream = open(infilename, "rb")
-    elif isinstance(infilename, bytes):
-        datastream = io.BytesIO(infilename)
-    else:
-        raise TypeError("infilename must be a filename, bytes or file-like object")
-    # Get the top level atom index
-    index = get_index(datastream)
-
-    mdat_pos = 999999
-    free_size = 0
-
-    # Make sure moov occurs AFTER mdat, otherwise no need to run!
-    for atom in index:
-        # The atoms are guaranteed to exist from get_index above!
-        if atom.name == "moov":
-            moov_atom = atom
-            moov_pos = atom.position
-        elif atom.name == "mdat":
-            mdat_pos = atom.position
-        elif atom.name == "free" and atom.position < mdat_pos:
-            # This free atom is before the mdat!
-            free_size += atom.size
-            log.info("Removing free atom at %d (%d bytes)" % (atom.position, atom.size))
-        elif atom.name == "\x00\x00\x00\x00" and atom.position < mdat_pos:
-            # This is some strange zero atom with incorrect size
-            free_size += 8
-            log.info("Removing strange zero atom at %s (8 bytes)" % atom.position)
-
-    # Offset to shift positions
-    offset = moov_atom.size - free_size
-
-    if moov_pos < mdat_pos:
-        # moov appears to be in the proper place, don't shift by moov size
-        offset -= moov_atom.size
-        if not free_size:
-            # No free atoms and moov is correct, we are done!
-            log.error("This file appears to already be setup for streaming!")
-            # Stupid hack to retrun the non-processed file:
-            if isinstance(infilename, str):
-                return open(infilename, "rb").read()
-            elif isinstance(infilename, bytes):
-                return io.BytesIO(infilename).read()
-            
-    # Read and fix moov
-    moov = _patch_moov(datastream, moov_atom, offset)
-
-    log.info("Writing output...")
-    outfile = b''
-
-    # Write ftype
-    for atom in index:
-        if atom.name == "ftyp":
-            log.debug("Writing ftyp... (%d bytes)" % atom.size)
-            datastream.seek(atom.position)
-            outfile += datastream.read(atom.size)
-
-    # Write moov
-    _bytes = moov.getvalue()
-    log.debug("Writing moov... (%d bytes)" % len(_bytes))
-    outfile += _bytes
-
-    # Write the rest
-    atoms = [item for item in index if item.name not in ["ftyp", "moov", "free"]]
-    for atom in atoms:
-        log.debug("Writing %s... (%d bytes)" % (atom.name, atom.size))
-        datastream.seek(atom.position)
-
-        # for compatability, allow '0' to mean no limit
-        cur_limit = limit or float('inf')
-        cur_limit = min(cur_limit, atom.size)
-
-        for chunk in get_chunks(datastream, CHUNK_SIZE, cur_limit):
-            outfile += chunk
-
-    return outfile
-
-
-def _patch_moov(datastream, atom, offset):
-    datastream.seek(atom.position)
-    moov = io.BytesIO(datastream.read(atom.size))
-
-    # reload the atom from the fixed stream
-    atom = _read_atom_ex(moov)
-
-    for atom in _find_atoms_ex(atom, moov):
-        # Read either 32-bit or 64-bit offsets
-        ctype, csize = dict(
-            stco=('L', 4),
-            co64=('Q', 8),
-        )[atom.name]
-
-        # Get number of entries
-        version, entry_count = struct.unpack(">2L", moov.read(8))
-
-        log.info("Patching %s with %d entries" % (atom.name, entry_count))
-
-        entries_pos = moov.tell()
-
-        struct_fmt = ">%(entry_count)s%(ctype)s" % vars()
-
-        # Read entries
-        entries = struct.unpack(struct_fmt, moov.read(csize * entry_count))
-
-        # Patch and write entries
-        offset_entries = [entry + offset for entry in entries]
-        moov.seek(entries_pos)
-        moov.write(struct.pack(struct_fmt, *offset_entries))
-    return moov
-
-def get_chunks(stream, chunk_size, limit):
-    remaining = limit
-    while remaining:
-        chunk = stream.read(min(remaining, chunk_size))
-        if not chunk:
-            return
-        remaining -= len(chunk)
-        yield chunk

scraibe/autotranscript.py

@@ -75,6 +75,11 @@ class Scraibe:
                                 Path to pyannote diarization model or model itself.
             **kwargs: Additional keyword arguments for whisper
                         and pyannote diarization models.
+                    e.g.:
+
+                    - 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.
         """
         
         
@@ -98,6 +103,15 @@ class Scraibe:
         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 = {}
+             
+            
     def autotranscribe(self, audio_file : Union[str, torch.Tensor, ndarray],
                    remove_original : bool = False,
                    **kwargs) -> Transcript:

scraibe/cli.py

@@ -9,7 +9,8 @@ from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
 import json
 
 from .autotranscript import Scraibe
-from .app.gradio_app import gradio_Interface
+from .misc import ParseKwargs
+
 
 from whisper.tokenizer import LANGUAGES , TO_LANGUAGE_CODE
 from torch.cuda import is_available
@@ -41,13 +42,15 @@ def cli():
                         help="List of audio files to transcribe.")
     
     group.add_argument('--start-server', action='store_true',
-                        help='Start the Gradio app.')
+                        help='Start the Gradio app.' \
+                        'If set, all other arguments are ignored' \
+                        'besides --server-config or --server-kwargs.')
     
-    parser.add_argument("--port", type=int, default= None,
+    parser.add_argument("--server-config", type=str, default= None,
-                        help="Port to run the Gradio app on. Defaults to 7860.")
+                        help="Path to the configy.yml file.")
     
-    parser.add_argument("--server-name", type=str, default= None,
+    parser.add_argument('--server-kwargs', nargs='*', action=ParseKwargs, default={},
-                        help="Name of the Gradio app. If empty 127.0.0.1 or 0.0.0.0 will be used.")
+                        help='Keyword arguments for the Gradio app.')
                         
     parser.add_argument("--whisper-model-name", default="medium",
                         help="Name of the Whisper model to use.")
@@ -66,7 +69,8 @@ def cli():
                         help="Device to use for PyTorch inference.")
 
     parser.add_argument("--num-threads", type=int, default=0,
-                        help="Number of threads used by torch for CPU inference; overrides MKL_NUM_THREADS/OMP_NUM_THREADS.")
+                        help="Number of threads used by torch for CPU inference; '\
+                            'overrides MKL_NUM_THREADS/OMP_NUM_THREADS.")
 
     parser.add_argument("--output-directory", "-o", type=str, default=".",
                         help="Directory to save the transcription outputs.")
@@ -113,8 +117,9 @@ def cli():
     if arg_dict["whisper_model_directory"]:
         class_kwargs["download_root"] = arg_dict.pop("whisper_model_directory")
 
-    model = Scraibe(**class_kwargs)
+    if not start_server:
         
+        model = Scraibe(**class_kwargs)
 
         if arg_dict["audio_files"]:
             audio_files = arg_dict.pop("audio_files")
@@ -158,10 +163,24 @@ def cli():
                         f.write(out)  
                     
     
-    if start_server: # unfinished code
+    else: # unfinished code
+        raise NotImplementedError("Currently not Working")
+        import subprocess
+        import sys
         
-        gradio_Interface(model).queue().launch(server_port=args.port, server_name=args.server_name)
+        execute_path = os.path.join(os.path.dirname(__file__), "app/app_starter.py")
         
+        config = arg_dict.pop("server_config")
+        server_kwargs = arg_dict.pop("server_kwargs")
+        
+        if not config:
+            subprocess.run([sys.executable, execute_path, f"--server-kwargs={server_kwargs}"])
+        elif not server_kwargs:
+            subprocess.run([sys.executable, execute_path, f"--server-config={config}"])
+        elif not config and not server_kwargs:
+            subprocess.run([sys.executable, execute_path])
+        else:
+            subprocess.run([sys.executable, execute_path, f"--server-config={config}", f"--server-kwargs={server_kwargs}"])
 
 if __name__ == "__main__":
     cli()

scraibe/diarisation.py

@@ -27,7 +27,9 @@ Usage:
     diarisation_output = model.diarization("path/to/audiofile.wav")
 """
 
+import warnings
 import os
+import yaml
 from pathlib import Path
 from typing import TypeVar, Union
 
@@ -216,6 +218,41 @@ class Diariser:
         if not os.path.exists(model) and use_auth_token is None:
             use_auth_token = cls._get_token()
             
+        elif os.path.exists(model) and not use_auth_token:
+            # check if model can be found locally nearby the config file
+            with open(model, 'r') as file:
+                config = yaml.safe_load(file)
+            
+            path_to_model = config['pipeline']['params']['segmentation']
+
+            if not os.path.exists(path_to_model):
+                warnings.warn(f"Model not found at {path_to_model}. "\
+                    "Trying to find it nearby the config file.")
+                
+                pwd = model.split("/")[:-1]
+                pwd = "/".join(pwd)
+                
+                path_to_model = os.path.join(pwd, "pytorch_model.bin")
+
+                if not os.path.exists(path_to_model):
+                    warnings.warn(f"Model not found at {path_to_model}. \
+                        'Trying to find it nearby .bin files instead.")
+                    # list elementes with the ending .bin
+                    bin_files = [f for f in os.listdir(pwd) if f.endswith(".bin")]
+                    if len(bin_files) == 1:
+                        path_to_model = os.path.join(pwd, bin_files[0])
+                    else:
+                        warnings.warn("Found more than one .bin file. "\
+                            "or none. Please specify the path to the model " \
+                            "or setup a huggingface token.")
+                                            
+                warnings.warn(f"Found model at {path_to_model} overwriting config file.")
+                
+                config['pipeline']['params']['segmentation'] = path_to_model
+                
+                with open(model, 'w') as file:
+                    yaml.dump(config, file)
+                              
         _model =  Pipeline.from_pretrained(model,
                                            use_auth_token = use_auth_token,
                                            cache_dir = cache_dir,

scraibe/misc.py

@@ -1,6 +1,7 @@
 import os
 import yaml
 from pyannote.audio.core.model import CACHE_DIR as PYANNOTE_CACHE_DIR
+from argparse import Action
 
 CACHE_DIR = os.getenv(
     "AUTOT_CACHE",
@@ -40,3 +41,17 @@ def config_diarization_yaml(file_path: str, path_to_segmentation: str = None) ->
 
     with open(file_path, "w") as stream:
         yaml.dump(yml, stream)
+
+class ParseKwargs(Action):
+    """
+    Custom argparse action to parse keyword arguments.
+    """
+    def __call__(self, parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, dict())
+        for value in values:
+            key, value = value.split('=')
+            try:
+                value = eval(value)
+            except:
+                pass
+            getattr(namespace, self.dest)[key] = value

setup.py

@@ -1,4 +1,3 @@
-from calendar import c
 import pkg_resources
 import os
 from setuptools import setup, find_packages
@@ -21,6 +20,8 @@ with open(verfile, "r") as fp:
 
 build_version = "SCRAIBE_BUILD" in os.environ
 
+version["ISRELEASED"] = True if "ISRELEASED" in os.environ else False
+
 if __name__ == "__main__":
 
     setup(
@@ -53,7 +54,7 @@ if __name__ == "__main__":
         keywords = ['transcription', 'speech recognition', 'whisper', 'pyannote', 'audio', 'ScrAIbe', 'scraibe',
                     'speech-to-text', 'speech-to-text transcription', 'speech-to-text recognition',
                     'voice-to-speech'],
-        package_data={'scraibe.app' : ["*.html", "*.svg"]},
+        package_data={'scraibe.app' : ["*.html", "*.svg","*.yml"]},
         entry_points={'console_scripts':
             ['scraibe = scraibe.cli:cli']}