whisper.py

from pydub import AudioSegment
import os
from transformers import WhisperForConditionalGeneration, WhisperProcessor, WhisperTokenizer
import torchaudio
import torch
import re
from transformers import pipeline
from peft import PeftModel, PeftConfig
import spaces

device = 0 if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float32

### Configuration
MODEL_NAME_V2 = "./whisper-large-v3-catalan"
MODEL_NAME_V1 = "projecte-aina/whisper-large-v3-tiny-caesar"
CHUNK_LENGTH = 30
BATCH_SIZE = 1

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME_V1,
    chunk_length_s=30,
    device=device,
    token=os.getenv("HF_TOKEN")
    ) 


peft_config = PeftConfig.from_pretrained(MODEL_NAME_V2)
model = WhisperForConditionalGeneration.from_pretrained(
    peft_config.base_model_name_or_path, 
    device_map="auto"
)

task = "transcribe"
    
model = PeftModel.from_pretrained(model, MODEL_NAME_V2)
model.config.use_cache = True
    
tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, task=task)
processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, task=task)
feature_extractor = processor.feature_extractor
forced_decoder_ids = processor.get_decoder_prompt_ids(task=task)
    
asr_pipe = pipeline(
    task="automatic-speech-recognition",
    model=model,
    tokenizer=tokenizer,
    feature_extractor=feature_extractor,
    chunk_length_s=30)

def asr(audio_path, task):
    asr_result = asr_pipe(audio_path, batch_size=BATCH_SIZE, generate_kwargs={"task":task}, return_timestamps=True)
    base_model = asr_pipe.model.base_model if hasattr(asr_pipe.model, "base_model") else asr_pipe.model
    return asr_result

def post_process_transcription(transcription, max_repeats=2):
    tokens = re.findall(r'\b\w+\'?\w*\b[.,!?]?', transcription)

    cleaned_tokens = []
    repetition_count = 0
    previous_token = None

    for token in tokens:
        reduced_token = re.sub(r"(\w{1,3})(\1{2,})", "", token)

        if reduced_token == previous_token:
            repetition_count += 1
            if repetition_count <= max_repeats:
                cleaned_tokens.append(reduced_token)
        else:
            repetition_count = 1
            cleaned_tokens.append(reduced_token)

        previous_token = reduced_token

    cleaned_transcription = " ".join(cleaned_tokens)
    cleaned_transcription = re.sub(r'\s+', ' ', cleaned_transcription).strip()

    return cleaned_transcription


def format_audio(audio_path):
    input_audio, sample_rate = torchaudio.load(audio_path)

    if input_audio.shape[0] == 2:  #stereo2mono
        input_audio = torch.mean(input_audio, dim=0, keepdim=True) 
    
    resampler = torchaudio.transforms.Resample(sample_rate, 16000)
    input_audio = resampler(input_audio)
    input_audio = input_audio.squeeze().numpy()
    return(input_audio)

def split_stereo_channels(audio_path):

    audio = AudioSegment.from_wav(audio_path)
    
    channels = audio.split_to_mono()
    if len(channels) != 2:
        raise ValueError(f"Audio {audio_path} does not have 2 channels.")

    channels[0].export(f"temp_mono_speaker1.wav", format="wav")  # Right
    channels[1].export(f"temp_mono_speaker2.wav", format="wav")  # Left

def transcribe_pipeline(audio, task):
    text = pipe(audio, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
    return text

def generate(audio_path, use_v2):
    
    if use_v2:
        split_stereo_channels(audio_path)

        audio_id = os.path.splitext(os.path.basename(audio_path))[0]

        left_channel_path = "temp_mono_speaker2.wav"
        right_channel_path = "temp_mono_speaker1.wav"

        left_audio = format_audio(left_channel_path)
        right_audio = format_audio(right_channel_path)

        left_result = asr(left_audio, task)
        right_result = asr(right_audio, task)

        left_segs = [(seg["timestamp"][0], seg["timestamp"][1], "Speaker 1", post_process_transcription(seg["text"])) for seg in left_result["chunks"]]
        right_segs = [(seg["timestamp"][0], seg["timestamp"][1], "Speaker 2", post_process_transcription(seg["text"])) for seg in right_result["chunks"]]

        merged_transcript = sorted(left_segs + right_segs, key=lambda x: x[0])
        merged_text = " ".join([seg[3] for seg in merged_transcript])
        
        output = ""
        for start, end, speaker, text in merged_transcript:
            output += f"[{start:.2f}s - {end:.2f}s] {speaker}: {text}\n"        
        
    else: 
        audio = AudioSegment.from_wav(audio_path)
        temp_mono_path = None

        if audio.channels != 1: #stereo2mono
            audio = audio.set_channels(1)
            temp_mono_path = "temp_mono.wav"
            audio.export(temp_mono_path, format="wav") 
            audio_path = temp_mono_path 
            task = "transcribe"
        output = transcribe_pipeline(format_audio(audio_path), task)
        
    clean_output = post_process_transcription(output, max_repeats=1) #check

    if temp_mono_path and os.path.exists(temp_mono_path):
        os.remove(temp_mono_path)
    
    for temp_file in ["temp_mono_speaker1.wav", "temp_mono_speaker2.wav"]:
        if os.path.exists(temp_file):
            os.remove(temp_file)
        
    return clean_output