from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torchaudio
import torch
import os
from pydub import AudioSegment


# Get the directory of the current file
current_dir = os.path.dirname(os.path.abspath(__file__))
# Construct the absolute path to the 'ffmpeg/bin' directory
ffmpeg_bin_path = os.path.join(current_dir, 'ffmpeg', 'bin')
# Add this path to the PATH environment variable
os.environ["PATH"] += os.pathsep + ffmpeg_bin_path
# Ensure ffmpeg is in PATH
AudioSegment.converter = os.path.join(ffmpeg_bin_path, 'ffmpeg.exe')


# load model and processor
processor = WhisperProcessor.from_pretrained("openai/whisper-small")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
model.config.forced_decoder_ids = None


def audio_to_text(webm_file_path):
    wav_file = "recorded_audio.wav"
    absolute_path = os.path.abspath(webm_file_path)

    # Load and convert audio
    # Check if the file exists
    if os.path.exists(webm_file_path):
        wav_audio = AudioSegment.from_file(absolute_path, format="webm")
        wav_audio.export(wav_file, format="wav")
        # Load the audio and resample it
        waveform, sample_rate = torchaudio.load('recorded_audio.wav')
        resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
        waveform = resampler(waveform)
        waveform = waveform.squeeze().numpy()
        input_features = processor(waveform, sampling_rate=16000, return_tensors="pt").input_features
        # generate token ids
        predicted_ids = model.generate(input_features)
        # decode token ids to text
        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
        return transcription
    else:
        return None