app.py

import soundfile as sf
import datetime
from pyctcdecode import BeamSearchDecoderCTC
import torch
import json
import os
import time
import gc
import gradio as gr
import librosa
from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM, AutoModelForSeq2SeqLM, AutoTokenizer, AutoProcessor
from huggingface_hub import hf_hub_download
from torchaudio.models.decoder import ctc_decoder
from numba import cuda

# load pretrained model
model = Wav2Vec2ForCTC.from_pretrained("facebook/mms-1b-all")
processor = AutoProcessor.from_pretrained("facebook/mms-1b-all")

lm_decoding_config = {}
lm_decoding_configfile = hf_hub_download(
    repo_id="facebook/mms-cclms",
    filename="decoding_config.json",
    subfolder="mms-1b-all",
)

with open(lm_decoding_configfile) as f:
    lm_decoding_config = json.loads(f.read())

# allow language model decoding for "eng"

decoding_config = lm_decoding_config["amh"]

lm_file = hf_hub_download(
    repo_id="facebook/mms-cclms",
    filename=decoding_config["lmfile"].rsplit("/", 1)[1],
    subfolder=decoding_config["lmfile"].rsplit("/", 1)[0],
)
token_file = hf_hub_download(
    repo_id="facebook/mms-cclms",
    filename=decoding_config["tokensfile"].rsplit("/", 1)[1],
    subfolder=decoding_config["tokensfile"].rsplit("/", 1)[0],
)
lexicon_file = None
if decoding_config["lexiconfile"] is not None:
    lexicon_file = hf_hub_download(
        repo_id="facebook/mms-cclms",
        filename=decoding_config["lexiconfile"].rsplit("/", 1)[1],
        subfolder=decoding_config["lexiconfile"].rsplit("/", 1)[0],
    )

beam_search_decoder = ctc_decoder(
    lexicon="./vocab_correct_cleaned.txt",
    tokens=token_file,
    lm=lm_file,
    nbest=1,
    beam_size=500,
    beam_size_token=50,
    lm_weight=float(decoding_config["lmweight"]),
    word_score=float(decoding_config["wordscore"]),
    sil_score=float(decoding_config["silweight"]),
    blank_token="<s>",
)

#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 ./audioToConvert.wav")

#Transcribe!!!
def Transcribe(file):
    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    start_time = time.time()
    model.load_adapter("amh")
    processor.tokenizer.set_target_lang("amh")

    preprocessAudio(file)
    block_size = 30
    batch_size = 8  # or whatever number you choose

    transcripts = []
    speech_segments = []

    stream = librosa.stream(
        "./audioToConvert.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
    encoding_end = 0
    sbv_file = open("subtitle.sbv", "w")

    # Define batch size
    batch_size = 11

    # Create an empty list to hold batches
    batch = []

    for speech_segment in stream:
        if len(speech_segment.shape) > 1:
            speech_segment = speech_segment[:,0] + speech_segment[:,1]

        # Add the current speech segment to the batch
        batch.append(speech_segment)

        # If the batch is full, process it
        if len(batch) == batch_size:
            # Concatenate all segments in the batch along the time axis
            input_values = processor(batch, sampling_rate=16_000, return_tensors="pt")
            input_values = input_values.to(device)
            with torch.no_grad():
                logits = model(**input_values).logits
            if len(logits.shape) == 1:
                logits = logits.unsqueeze(0)
            beam_search_result = beam_search_decoder(logits.to("cpu"))

            # Transcribe each segment in the batch
            for i in range(batch_size):
                transcription = " ".join(beam_search_result[i][0].words).strip()
                print(transcription)
                transcripts.append(transcription)

                encoding_end = encoding_start + block_size
                formatted_start = format_time(encoding_start)
                formatted_end = format_time(encoding_end)
                sbv_file.write(f"{formatted_start},{formatted_end}\n")
                sbv_file.write(f"{transcription}\n\n")

                encoding_start = encoding_end

            # Freeing up memory
            del input_values
            del logits
            del transcription
            torch.cuda.empty_cache()
            gc.collect()

            # Clear the batch
            batch = []

    if batch:
            # Concatenate all segments in the batch along the time axis
            input_values = processor(batch, sampling_rate=16_000, return_tensors="pt")
            input_values = input_values.to(device)
            with torch.no_grad():
                logits = model(**input_values).logits
            if len(logits.shape) == 1:
                logits = logits.unsqueeze(0)
            beam_search_result = beam_search_decoder(logits.to("cpu"))

            # Transcribe each segment in the batch
            for i in range(batch_size):
                transcription = " ".join(beam_search_result[i][0].words).strip()
                print(transcription)
                transcripts.append(transcription)

                encoding_end = encoding_start + block_size
                formatted_start = format_time(encoding_start)
                formatted_end = format_time(encoding_end)
                sbv_file.write(f"{formatted_start},{formatted_end}\n")
                sbv_file.write(f"{transcription}\n\n")

                encoding_start = encoding_end

            # Freeing up memory
            del input_values
            del logits
            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()
    print(f"The script ran for {end_time - start_time} seconds.")
    return("./subtitle.sbv")
    
demo = gr.Interface(fn=Transcribe, inputs=gr.File(label="Upload an audio file of Amharic content"), outputs=gr.File(label="Download .sbv transcription"),
                   title="Amharic Audio Transcription",
                    description="This application uses Meta MMS and a custom kenLM model to transcribe Amharic Audio files of arbitrary length into .sbv files. Upload an Amharic audio file and get your transcription! \n(Note: This is only a rough implementation of Meta's MMS for audio transcription, you should manually edit files after transcription has completed.)"
                   )
demo.launch()