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, 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],
)

beam_search_decoder = ctc_decoder(
    lexicon="./vocab_correct_cleaned.txt",
    tokens=token_file,
    lm=lm_file,
    nbest=1,
    beam_size=400,
    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):
    if isinstance(audioFile, str):  # If audioFile is a string (filepath)
        os.system(f"ffmpeg -y -i {audioFile} -ar 16000 ./audioToConvert.wav")
    else:  # If audioFile is an object with a name attribute
        os.system(f"ffmpeg -y -i {audioFile.name} -ar 16000 ./audioToConvert.wav")

#Transcribe!!!
def Transcribe(file):
    try:
        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 = 1
        filename = os.path.split(os.path.splitext(file.name)[0])[1]
        print(filename)

        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(f"{filename}_subtitle.sbv", "w")
        transcription_file = open(f"{filename}_transcription.txt", "w")

        # 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", padding=True)
                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()
                    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", padding=True)
                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(len(batch)):
                    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)
        transcription_file.write(f"{transcript}")
        sbv_file.close()
        transcription_file.close()

        end_time = time.time()
        print(f"The script ran for {end_time - start_time} seconds.")
        return([f"./{filename}_subtitle.sbv", f"./{filename}_transcription.txt"])
    except Exception as e:
        error_log = open("error_log.txt", "w")
        error_log.write(f"Exception occurred: {e}")
        error_log.close()


#Transcribe!!!
def TranscribeMic(file):
    try:
        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 = 1

        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(f"microphone_subtitle.sbv", "w")
        transcription_file = open(f"microphone_transcription.txt", "w")

        # 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", padding=True)
                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()
                    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", padding=True)
                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(len(batch)):
                    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)
        transcription_file.write(f"{transcript}")
        sbv_file.close()
        transcription_file.close()

        end_time = time.time()
        print(f"The script ran for {end_time - start_time} seconds.")
        return([f"./microphone_subtitle.sbv", f"./microphone_transcription.txt"])
    except Exception as e:
        error_log = open("error_log.txt", "w")
        error_log.write(f"Exception occurred: {e}")
        error_log.close()

demo = gr.Blocks()

with demo:
    gr.Markdown(
    """
    <div align="center">
    
    # Amharic Audio Transcription

    This application uses Meta MMS and an Amharic kenLM model to transcribe Amharic Audio files of arbitrary length into .sbv and .txt files. Upload an Amharic audio file and get your transcription!

    ### (Note: Transcription quality is quite low, you should review and edit transcriptions before making them publicly available)
    </div>
    """)
    with gr.Tabs():
        with gr.TabItem("From File"):
            with gr.Row():
                file_input = gr.File(label="Upload an audio file of Amharic Content")
                file_output = gr.Files(label="Download output files")
            file_button = gr.Button("Submit")
        with gr.TabItem("From Microphone"):
            with gr.Row():
                microphone_input = gr.Audio(type="filepath", source="microphone")
                microphone_output = gr.Files(label="Download output files")
            microphone_button = gr.Button("Submit")
            
    file_button.click(Transcribe, inputs=file_input, outputs=file_output)
    microphone_button.click(TranscribeMic, inputs=microphone_input, outputs=microphone_output)
        
# demo = gr.Interface(fn=Transcribe, inputs=[gr.File(label="Upload an audio file of Amharic content"), gr.Slider(0, 25, value=4, step=1, label="batch size", info="Approximately .5GB per batch")], 
#                     outputs=gr.File(label="Download .sbv transcription", file_count="multiple"),
#                     title="Amharic Audio Transcription",
#                     description="This application uses Meta MMS and an Amharic kenLM model to transcribe Amharic Audio files of arbitrary length into .sbv and .txt files. Upload an Amharic audio file and get your transcription! \n(Note: Transcription quality is quite low, you should review and edit transcriptions before making them publicly available)"
#                     )

demo.queue(concurrency_count=10)
demo.launch()