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import soundfile as sf
import datetime
from pyctcdecode import BeamSearchDecoderCTC
import torch
import os
import time
import gc
import gradio as gr
import librosa
from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM, AutoModelForSeq2SeqLM, AutoTokenizer
from numba import cuda
# load pretrained model
model = Wav2Vec2ForCTC.from_pretrained("facebook/mms-1b-all")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("jlonsako/mms-1b-all-AmhLM")
#Define Functions
#convert time into .sbv format
def format_time(seconds):
# Convert seconds to hh:mm:ss,ms format
return str(datetime.timedelta(seconds=seconds)).replace('.', ',')
#Convert Video/Audio into 16K wav file
def preprocessAudio(audioFile):
os.system(f"ffmpeg -y -i {audioFile.name} -ar 16000 ./audio.wav")
#Transcribe!!!
def Transcribe(file):
device = "cuda:0" if torch.cuda.is_available() else "cpu"
start_time = time.time()
model.load_adapter("amh")
preprocessAudio(file)
#os.system(f"ffmpeg -y -i ./July3_2023_Sermon.mov -ar 16000 ./audio.wav")
block_size = 30 #30 second chunks of audio
transcripts = []
stream = librosa.stream(
"./audio.wav",
block_length=block_size,
frame_length=16000,
hop_length=16000
)
model.to(device)
print(f"Model loaded to {device}: Entering transcription phase")
#Code for timestamping
encoding_start = 0
sbv_file = open("subtitle.sbv", "w")
for speech_segment in stream:
if len(speech_segment.shape) > 1:
speech_segment = speech_segment[:,0] + speech_segment[:,1]
input_values = processor(speech_segment, sampling_rate=16_000, return_tensors="pt").input_values.to(device)
with torch.no_grad():
logits = model(input_values).logits
if len(logits.shape) == 1:
print("test")
logits = logits.unsqueeze(0)
#predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(logits.cpu().numpy()).text
transcripts.append(transcription[0])
#Generate timestamps
encoding_end = encoding_start + block_size
formatted_start = format_time(encoding_start)
formatted_end = format_time(encoding_end)
#Write to the .sbv file
sbv_file.write(f"{formatted_start},{formatted_end}\n")
sbv_file.write(f"{transcription[0]}\n\n")
encoding_start = encoding_end
# Freeing up memory
del input_values
del logits
#del predicted_ids
del transcription
torch.cuda.empty_cache()
gc.collect()
# Join all transcripts into a single transcript
transcript = ' '.join(transcripts)
sbv_file.close()
end_time = time.time()
os.system("rm ./audio.wav")
print(f"The script ran for {end_time - start_time} seconds.")
return("./subtitle.sbv")
demo = gr.Interface(fn=Transcribe, inputs=gr.File(), "file")
#with gr.Blocks() as demo:
#file_output = gr.Textbox()
#upload_button = gr.UploadButton("Click to Upload a sermon",
# file_types=["video", "audio"], file_count="multiple")
#upload_button.upload(Transcribe, upload_button, file_output)
demo.launch()
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