Initial commit

.devcontainer/devcontainer.json

@@ -0,0 +1,21 @@
+{
+    "name": "Automatic speech recognition",
+    "build": {
+        "context": "..",
+        "dockerfile": "../Dockerfile"
+    },
+    "forwardPorts": [
+        7860
+    ],
+    "runArgs": [
+        "--gpus=all"
+    ],
+    "customizations": {
+        "vscode": {
+            // Add the IDs of extensions you want installed when the container is created.
+            "extensions": [
+                "ms-python.python"
+            ]
+        }
+    }
+}

.dockerignore

@@ -0,0 +1,3 @@
+venv
+.venv
+__pycache__

.env.example

@@ -0,0 +1 @@
+HF_AUTH_TOKEN=<hf_auth_token_here>

.github/dependabot.yml

@@ -0,0 +1,12 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for more information:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+# https://containers.dev/guide/dependabot
+
+version: 2
+updates:
+ - package-ecosystem: "devcontainers"
+   directory: "/"
+   schedule:
+     interval: weekly

.github/workflows/check-file-size.yml

@@ -0,0 +1,16 @@
+name: Check file size
+on: # or directly `on: [push]` to run the action on every push on any branch
+  pull_request:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check large files
+        uses: ActionsDesk/[email protected]
+        with:
+          filesizelimit: 10485760 # this is 10MB so we can sync to HF Spaces

.github/workflows/hugging-face-spaces.yml

@@ -0,0 +1,20 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push https://openeuro:[email protected]/spaces/openeuro/asr main

.gitignore

@@ -0,0 +1,5 @@
+venv
+.venv
+__pycache__
+.env
+tmp

COPYING

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
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+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
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+License will continue to apply to the part which is the covered work,
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
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+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
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+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
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+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

Dockerfile

@@ -0,0 +1,46 @@
+FROM pytorch/pytorch:2.1.2-cuda12.1-cudnn8-runtime
+
+ENV NB_USER jovyan
+ENV NB_UID 1000
+ENV NB_PREFIX /
+
+RUN export DEBIAN_FRONTEND=noninteractive \
+    && apt-get -yq update \
+    && apt-get -yq install --no-install-recommends \
+        git \
+        ffmpeg \
+        software-properties-common \
+    &&  apt upgrade -y \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN export DEBIAN_FRONTEND=noninteractive \
+    && add-apt-repository ppa:deadsnakes/ppa \
+    && apt-get -yq update \
+    && apt-get -yq install --no-install-recommends \
+        python3.10 \
+        python3.10-venv \
+    &&  apt upgrade -y \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN useradd -M -s /bin/bash -N -u ${NB_UID} ${NB_USER} \
+    && mkdir -p ${HOME} \
+    && chown -R ${NB_USER}:users ${HOME} \
+    && chown -R ${NB_USER}:users /usr/local/bin
+
+USER $NB_UID
+WORKDIR /home/${NB_USER}/
+ENV PATH=/venv/bin:$PATH
+
+COPY --chown=${NB_USER}:users ./requirements.txt /home/${NB_USER}/requirements.txt
+RUN python3.10 -m venv /home/${NB_USER}/venv \
+    && /home/${NB_USER}/venv/bin/pip install --upgrade pip wheel \
+    && /home/${NB_USER}/venv/bin/pip install -r /home/${NB_USER}/requirements.txt
+
+COPY --chown=${NB_USER}:users ./.env* /home/${NB_USER}/.env
+COPY --chown=${NB_USER}:users ./src /home/${NB_USER}/src
+
+EXPOSE 7860
+
+CMD ["/home/jovyan/venv/bin/python", "-u", "/home/jovyan/src/app.py"]

README.md

@@ -0,0 +1,97 @@
+---
+title: Automatic speech recognition
+sdk: gradio
+app_file: src/app.py
+python_version: 3.9
+sdk_version: 3.36.1
+app_port: 7860
+tags: [asr, stt, speech-to-text]
+pinned: true
+emoji: ✍️
+---
+
+# Automatic speech recognition
+
+[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
+![Python 3.10](badges/python3_10.svg)
+
+![Screenshot](img/screenshot.jpg)
+
+Automatic speech recognition uses [Distil-Whisper: distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2) to transcribe audio files and [pyannote-audio](https://github.com/pyannote/pyannote-audio) to add speaker diarization.
+
+It has optimized inference because of batching and Scale-Product-Attention (SDPA) or flash attention (if available).
+
+> :warning: **Always review transcriptions.** Transcriptions are done using AI models which are never 100% accurate.
+
+The repo contains (will contain) code to run the software
+
+- as a command-line tool
+- as graphical interface
+- as an inference API
+
+## Installation
+
+### Prerequisites
+
+The host machine must have an NVidia graphics card with CUDA 12.x installed natively, preferably [CUDA 12.1](https://developer.nvidia.com/cuda-12-1-0-download-archive), even when using Docker.
+
+The graphics card should have at least 8GB VRAM.
+
+The host machine must have Docker installed.
+
+For a Linux server, follow [these instructions](https://docs.docker.com/engine/install/)
+
+For a desktop (visual UI available), follow [these instructions](https://www.docker.com/products/docker-desktop/)
+
+### Docker (recommended)
+
+Build the Docker image
+
+`docker build -t asr .` (make sure Docker is running on your system)
+
+Run the Docker image, forward port 7860 (Gradio) and pass your GPU(s) to the container
+
+`docker run -p 7860:7860 --gpus all asr`
+
+Or in detached mode (in background)
+
+`docker run -d -p 7860:7860 --gpus all asr`
+
+You can check whether it is running with
+
+`docker ps`
+
+If you want to follow terminal output of a detached container, you can use
+
+`docker logs -f <first n digits of the container id>`
+
+The first time a transcription is requested, it will download the model.
+To avoid this happening each time, make sure you stop and start the same container, instead of using
+
+`docker run ...` again
+
+use `docker start <first n digits of container>`
+
+You can find the list of all containers, also stopped ones by using
+
+`docker ps -a`
+
+To open the app, open your **browser** and go to `localhost:7860`
+
+### Dev Container
+
+Open the project Visual Studio Code and use CTRL + SHIFT + P and type "Rebuild and reopen in container".
+
+After building, open up a terminal and activate the virtual environment
+
+`source /home/jovyan/venv/bin/activate`
+
+Then run the app
+
+`python src/app.py`
+
+## License
+
+GNU General Public License v3.0 or later
+
+See [COPYING](COPYING) to see the full text.

requirements.txt

@@ -0,0 +1,11 @@
+transformers==4.38.2
+accelerate==0.27.2
+gradio==4.21.0
+pyannote.audio==3.1.1
+python-docx==0.8.11
+pandas
+python-dotenv
+optimum
+--extra-index-url https://download.pytorch.org/whl/cu121
+torch==2.1.2
+torchaudio==2.1.2

src/app.py

@@ -0,0 +1,289 @@
+import os
+import sys
+module_path = os.path.abspath(os.path.join('..'))
+if module_path not in sys.path:
+    sys.path.append(module_path)
+from transcribe.transcribe import transcriber, languages
+import gradio as gr
+import torch
+import torchaudio
+import torch.cuda as cuda
+import platform
+from transformers import __version__ as transformers_version
+from dotenv import load_dotenv
+import shutil
+from docx import Document
+import logging
+import subprocess
+load_dotenv(override=True)
+
+logging.basicConfig(level=logging.INFO)
+
+HF_AUTH_TOKEN = os.getenv("HF_AUTH_TOKEN")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+num_gpus = cuda.device_count() if torch.cuda.is_available() else 0
+cuda_version = torch.version.cuda if torch.cuda.is_available() else "N/A"
+cudnn_version = torch.backends.cudnn.version() if torch.cuda.is_available() else "N/A"
+os_info = platform.system() + " " + platform.release() + " " + platform.machine()
+
+# Get the available VRAM for each GPU (if available)
+vram_info = []
+if torch.cuda.is_available():
+    for i in range(cuda.device_count()):
+        gpu_properties = cuda.get_device_properties(i)
+        vram_info.append(f"**GPU {i}: {gpu_properties.total_memory / 1024**3:.2f} GB**")
+
+pytorch_version = torch.__version__
+torchaudio_version = torchaudio.__version__ if 'torchaudio' in dir() else "N/A"
+
+device_info = f"""Running on: **{device}**
+
+    Number of GPUs available: **{num_gpus}**
+
+    CUDA version: **{cuda_version}**
+
+    CuDNN version: **{cudnn_version}**
+
+    PyTorch version: **{pytorch_version}**
+
+    Torchaudio version: **{torchaudio_version}**
+
+    Transformers version: **{transformers_version}**
+
+    Operating system: **{os_info}**
+
+    Available VRAM: 
+    \t {', '.join(vram_info) if vram_info else '**N/A**'}
+"""
+
+css = """
+#audio_input {
+    padding-bottom: 50px;
+}
+"""
+
+def format_srt_time(timestamp):
+    """Formats the timestamp into SRT time format."""
+    hours, remainder = divmod(timestamp, 3600)
+    minutes, seconds = divmod(remainder, 60)
+    milliseconds = int((seconds - int(seconds)) * 1000)
+    return f"{int(hours):02}:{int(minutes):02}:{int(seconds):02},{milliseconds:03}"
+
+def generate_srt_content(chunks):
+    """Generates the content for an SRT file based on transcription chunks."""
+    srt_content = ""
+    for i, chunk in enumerate(chunks, start=1):
+        try:
+            start, end = chunk["timestamp"]
+            start_time = format_srt_time(start)
+            end_time = format_srt_time(end)
+            text = chunk["text"]
+            srt_content += f"{i}\n{start_time} --> {end_time}\n{text}\n\n"
+        except:
+            logging.info("couldn't add phrase")
+            continue
+    return srt_content.strip()
+
+def create_black_screen_video(audio_file_path, output_video_path):
+    """
+    Creates a video with an empty black screen and the original audio from the input audio file.
+
+    Parameters:
+    - audio_file_path: Path to the input audio file.
+    - output_video_path: Path where the output video will be saved.
+    """
+    # Check if the output directory exists, create if not
+    output_dir = os.path.dirname(output_video_path)
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Construct the ffmpeg command
+    command = [
+        'ffmpeg',
+        '-y',  # Overwrite output file if it exists
+        '-f', 'lavfi',  # Input format
+        '-i', 'color=c=black:s=320x240:r=10',  # Generate a black color input, with 1280x720 resolution at 30 fps
+        '-i', audio_file_path,  # The input audio file
+        '-c:v', 'libx264',  # Video codec to use
+        '-tune', 'stillimage',  # Optimize for still image
+        '-c:a', 'aac',  # Audio codec to use
+        '-b:a', '192k',  # Audio bitrate
+        '-shortest',  # Finish encoding when the shortest input stream ends
+        output_video_path  # The output video file path
+    ]
+
+    # Execute the command
+    subprocess.run(command, check=True)
+
+
+def process_folder(files_source, model, language, translate, diarize, diarization_token):
+    output_folder_path = "./tmp"
+    if not os.path.exists(output_folder_path):
+        os.makedirs(output_folder_path)
+
+    for file_path in files_source:
+        # Check if the file is an audio file (e.g., .mp3, .mp4, .wav)
+        if file_path.endswith(('.mp3', '.mp4', '.wav')):
+ 
+            file_name = os.path.basename(file_path)
+
+            # Copy the original audio file to the output folder
+            output_audio_filepath = os.path.join(output_folder_path, file_name)
+            shutil.copy2(file_path, output_audio_filepath)
+
+    #             output_filename_base = os.path.splitext(filename)[0]
+    #             output_word_filepath = os.path.join(output_folder_path, output_filename_base + ".docx")
+    #             output_srt_filepath = os.path.join(output_folder_path, output_filename_base + ".srt")
+    #             output_summary_filepath = os.path.join(output_folder_path, output_filename_base + "_summary.docx")
+    #             output_video_filepath = os.path.join(output_folder_path, output_filename_base + ".mp4")
+    #             output_audio_filepath = os.path.join(output_folder_path, filename)
+
+    #             # Skip processing if any of the output files already exist
+    #             if os.path.exists(output_word_filepath) and os.path.exists(output_srt_filepath) and os.path.exists(output_summary_filepath) and os.path.exists(output_video_filepath) and os.path.exists(output_audio_filepath):
+    #                 print(f"Skipping {filename} as output files already exist.")
+    #                 continue
+                    
+            # Use the transcriber function to transcribe the audio file
+            transcription_result = transcriber(file_path, model, language=language, translate=translate, diarize=diarize, input_diarization_token=diarization_token)
+            print(transcription_result)
+    
+    #             transcribed_text = transcription_result["text"]
+    #             chunks = transcription_result.get("chunks", [])
+    
+    #             # Create a new Word document with the transcribed text
+    #             doc = Document()
+    #             for chunk in chunks:
+    #                 doc.add_paragraph(chunk["text"])
+    #             output_filename_base = os.path.splitext(filename)[0]
+    #             output_word_filepath = os.path.join(output_folder_path, output_filename_base + ".docx")
+    #             doc.save(output_word_filepath)
+    #             print(f"Transcription saved to {output_word_filepath}")
+    
+    #             # Create an SRT file with subtitles if chunks are available
+    #             if chunks:
+    #                 srt_content = generate_srt_content(chunks)
+    #                 output_srt_filepath = os.path.join(output_folder_path, output_filename_base + ".srt")
+    #                 with open(output_srt_filepath, "w", encoding='utf-8') as srt_file:
+    #                     srt_file.write(srt_content)
+    #                 print(f"Subtitles saved to {output_srt_filepath}")
+    
+    #             # Generate and save the summary
+    #             output_summary_filepath = os.path.join(output_folder_path, output_filename_base + "_summary.docx")
+
+    
+    #             # Create empty video
+    #             if filename.endswith(('.mp3', '.wav')):
+    #                 create_black_screen_video(file_path, os.path.join(output_folder_path, output_filename_base + ".mp4"))
+                
+    
+
+
+# def inference(input, diarize, num_speakers:int, strict, lan, trans, progress=gr.Progress()):
+def inference(input, model, language, translate, diarize, input_diarization_token):
+    tr = transcriber(input, model, language, translate, diarize, input_diarization_token)
+    return {textbox: gr.update(value=tr)}
+
+with gr.Blocks(title="Automatic speech recognition (beta)", css=css, analytics_enabled=False) as demo:
+    with gr.Row():
+        gr.Markdown(
+            """
+                # Automatic speech recognition
+
+                [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) 
+                
+                ![Python 3.10](https://raw.githubusercontent.com/tools4eu/automatic-speech-recognition/main/badges/python3_10.svg)
+
+                Report issues [here](https://github.com/tools4eu/automatic-speech-recognition/issues)
+            """
+
+        )
+    
+
+    with gr.Tab("Upload/record sound"):
+        with gr.Column():
+            dropdown_model = gr.Dropdown(
+                    label='Model', 
+                    choices = ["openai/whisper-large-v3", "openai/whisper-medium", "openai/whisper-small", "openai/whisper-tiny"],
+                    value="openai/whisper-large-v3", 
+                    info="""
+                        Larger models will increase the quality of the transcription, but reduce performance.
+                    """)
+        with gr.Row():
+            with gr.Column():
+                upl_input = gr.Audio(type='filepath', elem_id="audio_input")
+                upl_language = gr.Dropdown(
+                    label='Language', 
+                    choices = ['Automatic detection']+sorted(list(languages.keys())), 
+                    value='Automatic detection', 
+                    info="""
+                        Setting the language to "Automatic detection" will auto-detect the language based on the first 30 seconds.
+                        If the language is known upfront, always set it manually.
+                    """)
+
+        with gr.Row():
+            upl_translate = gr.Checkbox(label='Translate to English')
+
+        with gr.Column():
+            with gr.Group():
+                input_diarization_token = gr.Textbox(label='Paste your HF token here for speaker diarization (or add it as an environment variable)', value=HF_AUTH_TOKEN)
+                check_diarization = gr.Checkbox(label='Speaker diarization')
+                with gr.Accordion("For more details click here...", open=False):
+                    gr.Markdown("""
+                                    An access token can be created [here](https://hf.co/settings/tokens)
+                                
+                                    If not done yet for your account, you need to [accept segmentation terms & conditions](https://huggingface.co/pyannote/segmentation-3.0)
+                                
+                                    If not done yet for your account, you need to [accept diarization terms & conditions](https://huggingface.co/pyannote/speaker-diarization-3.1)
+                                """)
+        
+        with gr.Row():
+            upl_btn = gr.Button("Transcribe")
+        
+        with gr.Row(variant='panel'):
+            with gr.Column():
+                textbox = gr.Textbox(label='Transciption',visible=True)
+
+    with gr.Tab("Process multiple files"):
+        files_source=gr.Files(label="Select Audio Files", file_count="multiple")
+        with gr.Column():
+            dropdown_model_multi = gr.Dropdown(
+                    label='Model', 
+                    choices = ["openai/whisper-large-v3", "openai/whisper-medium", "openai/whisper-small", "openai/whisper-tiny"],
+                    value="openai/whisper-large-v3", 
+                    info="""
+                        Larger models will increase the quality of the transcription, but reduce performance.
+                    """)
+        dropdown_lang_multi = gr.Dropdown(
+                    label='Language', 
+                    choices = ['Automatic detection']+sorted(list(languages.keys())), 
+                    value='Automatic detection', 
+                    info="""
+                        Setting the language to "Automatic detection" will auto-detect the language based on the first 30 seconds.
+                        If the language is known upfront, always set it manually.
+                    """)
+        checkbox_trans_multi = gr.Checkbox(label='Translate to English')
+        with gr.Column():
+            with gr.Group():
+                input_diarization_token_multi = gr.Textbox(label='Paste your Hugging Face token here for speaker diarization (or add it as an environment variable)', value=HF_AUTH_TOKEN)
+                check_diarization_multi = gr.Checkbox(label='Speaker diarization')
+                with gr.Accordion("For more details click here...", open=False):
+                    gr.Markdown("""
+                                    An access token can be created [here](https://hf.co/settings/tokens)
+                                
+                                    If not done yet for your account, you need to [accept segmentation terms & conditions](https://huggingface.co/pyannote/segmentation-3.0)
+                                
+                                    If not done yet for your account, you need to [accept diarization terms & conditions](https://huggingface.co/pyannote/speaker-diarization-3.1)
+                                """)
+        btn_transcribe_multi= gr.Button("Transcribe")
+        textbox_transcribe_multi= gr.Chatbot(label='Transciption',visible=True)
+
+    with gr.Tab("Device info"):
+        gr.Markdown(device_info, label="Hardware info & installed packages")
+        # gr.Markdown(device_info, label="Hardware info & installed packages", lines=len(device_info.split("\n")), container=False)
+
+    transcribe_event = upl_btn.click(fn=inference, inputs=[upl_input, dropdown_model, upl_language, upl_translate, check_diarization, input_diarization_token], outputs=[textbox], concurrency_limit=1)
+    # transcribe_files_event = btn_transcribe_folder.click(fn=process_folder, inputs=[files_source, dropdown_lang_multi, checkbox_trans_multi, input_diarization_token], outputs=[textbox_transcribe_folder], concurrency_limit=1)
+    transcribe_files_event = btn_transcribe_multi.click(fn=process_folder, inputs=[files_source, dropdown_model_multi, dropdown_lang_multi, check_diarization_multi, checkbox_trans_multi, input_diarization_token_multi], outputs=[], concurrency_limit=1)
+
+demo.queue().launch(server_name="0.0.0.0")