import torch from speechbrain.inference.interfaces import Pretrained import librosa import numpy as np class ASR(Pretrained): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def encode_batch(self, device, wavs, wav_lens=None, normalize=False): wavs = wavs.to(device) wav_lens = wav_lens.to(device) # Forward pass encoded_outputs = self.mods.encoder_w2v2(wavs.detach()) # append tokens_bos = torch.zeros((wavs.size(0), 1), dtype=torch.long).to(device) embedded_tokens = self.mods.embedding(tokens_bos) decoder_outputs, _ = self.mods.decoder(embedded_tokens, encoded_outputs, wav_lens) # Output layer for seq2seq log-probabilities predictions = self.hparams.test_search(encoded_outputs, wav_lens)[0] # predicted_words = [self.hparams.tokenizer.decode_ids(prediction).split(" ") for prediction in predictions] predicted_words = [] for prediction in predictions: prediction = [token for token in prediction if token != 0] predicted_words.append(self.hparams.tokenizer.decode_ids(prediction).split(" ")) prediction = [] for sent in predicted_words: sent = self.filter_repetitions(sent, 3) prediction.append(sent) predicted_words = prediction return predicted_words def filter_repetitions(self, seq, max_repetition_length): seq = list(seq) output = [] max_n = len(seq) // 2 for n in range(max_n, 0, -1): max_repetitions = max(max_repetition_length // n, 1) # Don't need to iterate over impossible n values: # len(seq) can change a lot during iteration if (len(seq) <= n*2) or (len(seq) <= max_repetition_length): continue iterator = enumerate(seq) # Fill first buffers: buffers = [[next(iterator)[1]] for _ in range(n)] for seq_index, token in iterator: current_buffer = seq_index % n if token != buffers[current_buffer][-1]: # No repeat, we can flush some tokens buf_len = sum(map(len, buffers)) flush_start = (current_buffer-buf_len) % n # Keep n-1 tokens, but possibly mark some for removal for flush_index in range(buf_len - buf_len%n): if (buf_len - flush_index) > n-1: to_flush = buffers[(flush_index + flush_start) % n].pop(0) else: to_flush = None # Here, repetitions get removed: if (flush_index // n < max_repetitions) and to_flush is not None: output.append(to_flush) elif (flush_index // n >= max_repetitions) and to_flush is None: output.append(to_flush) buffers[current_buffer].append(token) # At the end, final flush current_buffer += 1 buf_len = sum(map(len, buffers)) flush_start = (current_buffer-buf_len) % n for flush_index in range(buf_len): to_flush = buffers[(flush_index + flush_start) % n].pop(0) # Here, repetitions just get removed: if flush_index // n < max_repetitions: output.append(to_flush) seq = [] to_delete = 0 for token in output: if token is None: to_delete += 1 elif to_delete > 0: to_delete -= 1 else: seq.append(token) output = [] return seq # def classify_file(self, path): # # waveform = self.load_audio(path) # waveform, sr = librosa.load(path, sr=16000) # waveform = torch.tensor(waveform) # # Fake a batch: # batch = waveform.unsqueeze(0) # rel_length = torch.tensor([1.0]) # outputs = self.encode_batch(batch, rel_length) # return outputs def classify_file(self, path, device): # Load the audio file # path = "long_sample.wav" waveform, sr = librosa.load(path, sr=16000) # Get audio length in seconds audio_length = len(waveform) / sr if audio_length >= 20: print(f"Audio is too long ({audio_length:.2f} seconds), splitting into segments") # Detect non-silent segments non_silent_intervals = librosa.effects.split(waveform, top_db=20) # Adjust top_db for sensitivity segments = [] current_segment = [] current_length = 0 max_duration = 20 * sr # Maximum segment duration in samples (20 seconds) for interval in non_silent_intervals: start, end = interval segment_part = waveform[start:end] # If adding the next part exceeds max duration, store the segment and start a new one if current_length + len(segment_part) > max_duration: segments.append(np.concatenate(current_segment)) current_segment = [] current_length = 0 current_segment.append(segment_part) current_length += len(segment_part) # Append the last segment if it's not empty if current_segment: segments.append(np.concatenate(current_segment)) # Process each segment outputs = [] for i, segment in enumerate(segments): print(f"Processing segment {i + 1}/{len(segments)}, length: {len(segment) / sr:.2f} seconds") segment_tensor = torch.tensor(segment).to(device) # Fake a batch for the segment batch = segment_tensor.unsqueeze(0).to(device) rel_length = torch.tensor([1.0]).to(device) # Adjust if necessary # Pass the segment through the ASR model segment_output = self.encode_batch(device, batch, rel_length) yield segment_output else: waveform = torch.tensor(waveform).to(device) waveform = waveform.to(device) # Fake a batch: batch = waveform.unsqueeze(0) rel_length = torch.tensor([1.0]).to(device) outputs = self.encode_batch(device, batch, rel_length) yield outputs def classify_file_whisper(self, path, pipe, device): waveform, sr = librosa.load(path, sr=16000) transcription = pipe(waveform, generate_kwargs={"language": "macedonian"})["text"] return transcription def classify_file_mms(self, path, processor, model, device): # Load the audio file waveform, sr = librosa.load(path, sr=16000) # Get audio length in seconds audio_length = len(waveform) / sr if audio_length >= 20: print(f"MMS Audio is too long ({audio_length:.2f} seconds), splitting into segments") # Detect non-silent segments non_silent_intervals = librosa.effects.split(waveform, top_db=20) # Adjust top_db for sensitivity segments = [] current_segment = [] current_length = 0 max_duration = 20 * sr # Maximum segment duration in samples (20 seconds) for interval in non_silent_intervals: start, end = interval segment_part = waveform[start:end] # If adding the next part exceeds max duration, store the segment and start a new one if current_length + len(segment_part) > max_duration: segments.append(np.concatenate(current_segment)) current_segment = [] current_length = 0 current_segment.append(segment_part) current_length += len(segment_part) # Append the last segment if it's not empty if current_segment: segments.append(np.concatenate(current_segment)) # Process each segment outputs = [] for i, segment in enumerate(segments): print(f"MMS Processing segment {i + 1}/{len(segments)}, length: {len(segment) / sr:.2f} seconds") segment_tensor = torch.tensor(segment).to(device) # Pass the segment through the ASR model inputs = processor(segment_tensor, sampling_rate=16_000, return_tensors="pt").to(device) outputs = model(**inputs).logits ids = torch.argmax(outputs, dim=-1)[0] segment_output = processor.decode(ids) yield segment_output else: waveform = torch.tensor(waveform).to(device) inputs = processor(waveform, sampling_rate=16_000, return_tensors="pt").to(device) outputs = model(**inputs).logits ids = torch.argmax(outputs, dim=-1)[0] transcription = processor.decode(ids) yield transcription