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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 (beta)
[](https://www.gnu.org/licenses/gpl-3.0)
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") |