import os
# os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
import spaces
import gc
from functools import partial
import gradio as gr
import torch
from speechbrain.inference.interfaces import Pretrained, foreign_class
from transformers import T5Tokenizer, T5ForConditionalGeneration
import librosa
import whisper_timestamped as whisper
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline, Wav2Vec2ForCTC, AutoProcessor
from speechbrain.inference.VAD import VAD
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.backends.cuda.matmul.allow_tf32 = True
# Load the VAD model
vad_model = VAD.from_hparams(
source="speechbrain/vad-crdnn-libriparty",
savedir="vad_model",
)
def clean_up_memory():
gc.collect()
torch.cuda.empty_cache()
@spaces.GPU(duration=15)
def recap_sentence(string):
# Restore capitalization and punctuation using the model
inputs = recap_tokenizer(["restore capitalization and punctuation: " + string], return_tensors="pt", padding=True).to(device)
outputs = recap_model.generate(**inputs, max_length=768, num_beams=5, early_stopping=True).squeeze(0)
recap_result = recap_tokenizer.decode(outputs, skip_special_tokens=True)
return recap_result
@spaces.GPU(duration=30)
def return_prediction_w2v2_mic(mic=None, vad_model=vad_model, device=device):
if mic is not None:
download_path = mic.split(".")[0] + ".txt"
w2v2_result = w2v2_classifier.classify_file_w2v2(mic, vad_model, device)
else:
recap_result = ""
w2v2_result = ""
download_path = "empty.txt"
with open(download_path, "w") as f:
f.write(recap_result)
yield recap_result, download_path
recap_result = ""
prev_segment = ""
prev_segment_len = 0
for k, segment in enumerate(w2v2_result):
if prev_segment == "":
recap_segment = recap_sentence(segment)
else:
prev_segment_len = len(prev_segment.split())
recap_segment = recap_sentence(prev_segment + " " + segment)
# remove prev_segment from the beginning of the recap_result
recap_segment = recap_segment.split()
recap_segment = recap_segment[prev_segment_len:]
recap_segment = " ".join(recap_segment)
prev_segment = segment[0]
recap_result += recap_segment + " "
# If the letter after punct is small, recap it
for i, letter in enumerate(recap_result):
if i > 1 and recap_result[i-2] in [".", "!", "?"] and letter.islower():
recap_result = recap_result[:i] + letter.upper() + recap_result[i+1:]
clean_up_memory()
with open(download_path, "w") as f:
f.write(recap_result)
yield recap_result, download_path
@spaces.GPU(duration=30)
def return_prediction_w2v2_file(file=None, vad_model=vad_model, device=device):
if file is not None:
download_path = file.split(".")[0] + ".txt"
w2v2_result = w2v2_classifier.classify_file_w2v2(file, vad_model, device)
else:
recap_result = ""
w2v2_result = ""
download_path = "empty.txt"
with open(download_path, "w") as f:
f.write(recap_result)
yield recap_result, download_path
recap_result = ""
prev_segment = ""
prev_segment_len = 0
for k, segment in enumerate(w2v2_result):
if prev_segment == "":
recap_segment= recap_sentence(segment)
else:
prev_segment_len = len(prev_segment.split())
recap_segment = recap_sentence(prev_segment + " " + segment)
# remove prev_segment from the beginning of the recap_result
recap_segment = recap_segment.split()
recap_segment = recap_segment[prev_segment_len:]
recap_segment = " ".join(recap_segment)
prev_segment = segment[0]
recap_result += recap_segment + " "
# If the letter after punct is small, recap it
for i, letter in enumerate(recap_result):
if i > 1 and recap_result[i-2] in [".", "!", "?"] and letter.islower():
recap_result = recap_result[:i] + letter.upper() + recap_result[i+1:]
clean_up_memory()
with open(download_path, "w") as f:
f.write(recap_result)
yield recap_result, download_path
# Create a partial function with the device pre-applied
return_prediction_w2v2_mic_with_device = partial(return_prediction_w2v2_mic, vad_model=vad_model, device=device)
return_prediction_w2v2_file_with_device = partial(return_prediction_w2v2_file, vad_model=vad_model, device=device)
# Load the ASR models
w2v2_classifier = foreign_class(source="Macedonian-ASR/buki-wav2vec2-2.0", pymodule_file="custom_interface_app.py", classname="ASR")
w2v2_classifier = w2v2_classifier.to(device)
w2v2_classifier.eval()
# Load the T5 tokenizer and model for restoring capitalization
recap_model_name = "Macedonian-ASR/mt5-restore-capitalization-macedonian"
recap_tokenizer = T5Tokenizer.from_pretrained(recap_model_name)
recap_model = T5ForConditionalGeneration.from_pretrained(recap_model_name, torch_dtype=torch.float16)
recap_model.to(device)
recap_model.eval()
mic_transcribe_wav2vec2 = gr.Interface(
fn=return_prediction_w2v2_mic_with_device,
inputs=gr.Audio(sources="microphone", type="filepath"),
outputs=[gr.Textbox(label="Транскрипција"), gr.File(label="Зачувај го транскриптот", file_count="single")],
allow_flagging="never",
live=True
)
file_transcribe_wav2vec2 = gr.Interface(
fn=return_prediction_w2v2_file_with_device,
inputs=gr.Audio(sources="upload", type="filepath"),
outputs=[gr.Textbox(label="Транскрипција"), gr.File(label="Зачувај го транскриптот", file_count="single")],
allow_flagging="never",
live=True
)
project_description_header = '''
'''
project_description_footer = '''
'''
css = """
.gradio-container {
background-color: #f3f3f3 !important;
display: flex;
flex-direction: column;
}
.custom-markdown p, .custom-markdown li, .custom-markdown h2, .custom-markdown a, .custom-markdown strong {
font-size: 15px !important;
font-family: Arial, sans-serif !important;
color: black !important; /* Ensure text is black */
}
button {
color: orange !important;
}
.header {
order: 1;
margin-bottom: 20px;
}
.main-content {
order: 2;
}
.footer {
order: 3;
margin-top: 20px;
}
.footer h2, .footer li, strong {
color: black !important; /* Ensure footer text is also black */
}
.header h2, .header h4, .header li, strong {
color: black !important; /* Ensure footer text is also black */
}
"""
transcriber_app = gr.Blocks(css=css, delete_cache=(60, 120))
with transcriber_app:
state = gr.State()
# gr.HTML('
')
gr.HTML(project_description_header)
gr.TabbedInterface(
[mic_transcribe_wav2vec2, file_transcribe_wav2vec2],
["Буки-w2v2 транскрипција од микрофон", "Буки-w2v2 транскрипција од фајл"],
)
gr.HTML(project_description_footer)
state = gr.State(value=[], delete_callback=lambda v: print("STATE DELETED"))
transcriber_app.unload(return_prediction_w2v2_mic_with_device)
transcriber_app.unload(return_prediction_w2v2_file_with_device)
# transcriber_app.launch(debug=True, share=True, ssl_verify=False)
if __name__ == "__main__":
transcriber_app.queue()
transcriber_app.launch()