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import whisper
import datetime
import gradio as gr
import pandas as pd
import time
import os
import numpy as np
from sklearn.cluster import AgglomerativeClustering
import torch
from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
from pyannote.audio import Audio, Pipeline
from pyannote.core import Segment
from gpuinfo import GPUInfo
from util import *
import wave
import contextlib
from transformers import pipeline
import psutil
source_language_list = [key[0] for key in source_languages.items()]
MODEL_NAME = "openai/whisper-base.en"
lang = "en"
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
embedding_model = PretrainedSpeakerEmbedding(
"speechbrain/spkrec-ecapa-voxceleb",
device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1",
use_auth_token="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")
def transcribe(microphone, file_upload):
print("Beginning transcribe...")
warn_output = ""
if (microphone is not None) and (file_upload is not None):
warn_output = (
"WARNING: You've uploaded an audio file and used the microphone. "
"The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
)
elif (microphone is None) and (file_upload is None):
return "ERROR: You have to either use the microphone or upload an audio file"
file = microphone if microphone is not None else file_upload
text = pipe(file)["text"]
return warn_output + text
def convert_time(secs):
return datetime.timedelta(seconds=round(secs))
def speech_to_text(audio_file_path, selected_source_lang, whisper_model, num_speakers, output_types=['csv','docx','md']):
"""
# Transcribe youtube link using OpenAI Whisper
1. Using Open AI's Whisper model to seperate audio into segments and generate transcripts.
2. Generating speaker embeddings for each segments.
3. Applying agglomerative clustering on the embeddings to identify the speaker for each segment.
Speech Recognition is based on models from OpenAI Whisper https://github.com/openai/whisper
Speaker diarization model and pipeline from by https://github.com/pyannote/pyannote-audio
"""
print("Loading model...")
torch.cuda.empty_cache()
model = whisper.load_model(whisper_model)
time_start = time.time()
try:
upload_name = audio_file_path.orig_name
file_name = audio_file_path.name
except:
upload_name = "output.mp3"
file_name = audio_file_path
if(audio_file_path == None):
raise ValueError("Error no video input")
try:
_,file_ending = os.path.splitext(f'{file_name}')
print(f'file ending is {file_ending}')
audio_file = file_name.replace(file_ending, ".wav")
print("starting conversion to wav")
os.system(f'ffmpeg -y -i "{file_name}" -ar 16000 -ac 1 -c:a pcm_s16le "{audio_file}"')
# Get duration
with contextlib.closing(wave.open(audio_file,'r')) as f:
frames = f.getnframes()
rate = f.getframerate()
duration = frames / float(rate)
print(f"conversion to wav ready, duration of audio file: {duration}")
# Transcribe audio
options = dict(language=selected_source_lang, beam_size=5, best_of=5)
transcribe_options = dict(task="transcribe", **options)
result = model.transcribe(audio_file, **transcribe_options)
segments = result["segments"]
print("starting whisper done with whisper")
except Exception as e:
raise RuntimeError("Error converting video to audio")
try:
# Create embedding
def segment_embedding(segment):
audio = Audio()
start = segment["start"]
# Whisper overshoots the end timestamp in the last segment
end = min(duration, segment["end"])
clip = Segment(start, end)
waveform, sample_rate = audio.crop(audio_file, clip)
return embedding_model(waveform[None])
embeddings = np.zeros(shape=(len(segments), 192))
for i, segment in enumerate(segments):
embeddings[i] = segment_embedding(segment)
embeddings = np.nan_to_num(embeddings)
print(f'Embedding shape: {embeddings.shape}')
# Assign speaker label
clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
labels = clustering.labels_
for i in range(len(segments)):
segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
# Make output
objects = {
'Start' : [],
'End': [],
'Speaker': [],
'Text': []
}
text = ''
for (i, segment) in enumerate(segments):
if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
objects['Start'].append(str(convert_time(segment["start"])))
objects['Speaker'].append(segment["speaker"])
if i != 0:
objects['End'].append(str(convert_time(segments[i - 1]["end"])))
objects['Text'].append(text)
text = ''
text += segment["text"] + ' '
objects['End'].append(str(convert_time(segments[i - 1]["end"])))
objects['Text'].append(text)
time_end = time.time()
time_diff = time_end - time_start
memory = psutil.virtual_memory()
gpu_utilization, gpu_memory = GPUInfo.gpu_usage()
gpu_utilization = gpu_utilization[0] if len(gpu_utilization) > 0 else 0
gpu_memory = gpu_memory[0] if len(gpu_memory) > 0 else 0
system_info = f"""
*Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB.*
*Processing time: {time_diff:.5} seconds.*
*GPU Utilization: {gpu_utilization}%, GPU Memory: {gpu_memory}MiB.*
"""
os.remove(file_name)
print(output_types)
docx = not set(['docx']).isdisjoint(output_types)
markdown = not set(['md']).isdisjoint(output_types)
csv = not set(['csv']).isdisjoint(output_types)
other_outs = zip_files(otheroutputs(objects, csv=csv, markdown=markdown, docx=docx,upload_name=upload_name))
return pd.DataFrame(objects), system_info, other_outs
except Exception as e:
raise RuntimeError("Error Running inference with local model", e)
def main():
df_init = pd.DataFrame(columns=['Start', 'End', 'Speaker', 'Text'])
memory = psutil.virtual_memory()
try:
cuda_device_model = {torch.cuda.get_device_name(torch.cuda.current_device())}
except:
cuda_device_model = "CUDA not found"
system_info = gr.Markdown(f"*Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB* Have CUDA?: {torch.cuda.is_available()} CUDA Device: {cuda_device_model}")
transcription_df = gr.DataFrame(value=df_init,label="Transcription dataframe", row_count=(0, "dynamic"), max_rows = 10, wrap=True, overflow_row_behaviour='paginate')
zip_download = gr.File(label="Output")
title = "Whisper speaker diarization"
demo = gr.Blocks(title=title)
demo.queue(concurrency_count=3)
demo.encrypt = False
selected_source_lang = gr.Dropdown(choices=source_language_list, type="value", value="en", label="Spoken language in recording", interactive=True)
selected_whisper_model = gr.Dropdown(choices=whisper_models, type="value", value="base", label="Selected Whisper model", interactive=True)
number_speakers = gr.Number(precision=0, value=2, label="Selected number of speakers", interactive=True)
out_formats = ["docx","md","csv"]
output_types = gr.CheckboxGroup(choices=out_formats, value=out_formats, label="Select output types", interactive=True)
with demo:
with gr.Tab("Transcribe Audio Files"):
with gr.Row():
gr.HTML('<script defer data-domain="transcribe.orgmycology.com" src="https://a.duckles.nz/js/plausible.js"></script>')
gr.Markdown("""## Transcribe your audio files
This tool will help you transcribe audio files, tag the speakers (i.e. Speaker 1, Speaker 2).
Steps:
1. Upload file (drag/drop to upload area or click and select)
2. Select language
2. Select model version (larger size == slower, but higher accuracy)
3. Hint at the number of speakers in the audio file (doesn't have to be exact)
3. Choose output formats you'd like
4. Click Transcribe!
5. Wait for it to finish, and download the outputfile
""")
with gr.Row():
with gr.Column():
upload_diarize = gr.File(type="file", label="Upload Audio", interactive=True)
with gr.Row():
with gr.Column():
selected_source_lang.render()
selected_whisper_model.render()
number_speakers.render()
output_types.render()
transcribe_btn = gr.Button(" 🟢 Transcribe! ")
transcribe_btn.click(speech_to_text, [upload_diarize, selected_source_lang, selected_whisper_model, number_speakers, output_types], [transcription_df, system_info, zip_download], api_name="diarized_transcribe")
with gr.Row():
with gr.Column():
zip_download.render()
transcription_df.render()
system_info.render()
demo.launch(show_error=True, debug=True)
if __name__ == "__main__":
import sys
input_file = sys.argv[1]
selected_source_lang = "en"
selected_whisper_model = "base"
number_speakers = 2
speech_to_text(input_file, selected_source_lang, selected_whisper_model, number_speakers )
else:
main()
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