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import logging
import soundfile as sf
import torch
import torchaudio
from transformers import Wav2Vec2Processor
from constants import MAX_AUDIO_DURATION_SECONDS, MONO_CHANNEL, SAMPLING_RATE
from gop_model import GOPPhonemeClassifier
logger = logging.getLogger(__name__)
def load_model_and_processor(model_repo_id: str):
logger.info("Loading model and processor from Hugging Face Hub: %s", model_repo_id)
model = GOPPhonemeClassifier.from_pretrained(
model_repo_id,
device_map="auto",
)
processor = Wav2Vec2Processor.from_pretrained(model_repo_id)
model.eval()
return model, processor
def validate_phonemes(phoneme_text, allowed_phonemes):
if not phoneme_text.strip():
return "<p style='text-align:center; color:red;'>Please enter the phonemes.</p>"
phonemes = phoneme_text.strip().split()
for phoneme in phonemes:
if phoneme not in allowed_phonemes:
return f"<p style='text-align:center; color:red;'>Invalid phoneme: '{phoneme}'. Please check your input.</p>"
return None
def _prepare_canonical_tokens(transcript: str, processor: Wav2Vec2Processor, device: torch.device):
phonemes: List[str] = transcript.strip().split()
if not phonemes:
raise ValueError("Please enter at least one phoneme.")
token_mask_values = [token != "|" for token in phonemes]
if not any(token_mask_values):
raise ValueError("The phoneme sequence must contain at least one non-boundary token.")
tokenizer = processor.tokenizer
unk_id = getattr(tokenizer, "unk_token_id", None)
ids = tokenizer.convert_tokens_to_ids(phonemes)
if isinstance(ids, int):
ids = [ids]
ids = [token_id if token_id is not None else unk_id for token_id in ids]
canonical_token_ids = torch.tensor([ids], dtype=torch.long, device=device)
token_lengths = torch.tensor([len(ids)], dtype=torch.long, device=device)
token_mask = torch.tensor([token_mask_values], dtype=torch.bool, device=device)
display_tokens = [token for token, is_active in zip(phonemes, token_mask_values) if is_active]
return canonical_token_ids, token_lengths, token_mask, display_tokens
def _extract_head_predictions(
logits_by_head: Dict[str, torch.Tensor],
token_mask: torch.Tensor,
display_tokens: List[str],
deltas: Dict[str, float] | None = None,
correct_index: int = 0,
) -> Dict[str, Tuple[List[int], List[str]]]:
active_mask = token_mask[0].bool()
results: Dict[str, Tuple[List[int], List[str]]] = {}
head_deltas = deltas or {}
for head_name, head_logits in logits_by_head.items():
predicted_scores = _predict_scores(
head_logits,
delta=head_deltas.get(head_name),
correct_index=correct_index,
)[0]
filtered_scores = predicted_scores[active_mask].detach().cpu().tolist()
results[head_name] = (filtered_scores, display_tokens)
return results
def parse_delta_value(value):
if value is None or value == "":
return None
try:
delta = float(value)
except (TypeError, ValueError):
logger.warning("Invalid delta value %r; ignoring.", value)
return None
if delta <= 0:
return None
return delta
def _predict_scores(scores_tensor, delta=None, correct_index=0):
num_classes = scores_tensor.size(-1)
use_delta = delta is not None and delta > 0 and 0 <= correct_index < num_classes
if use_delta:
probs = torch.softmax(scores_tensor, dim=-1)
correct_probs = probs[..., correct_index]
incorrect_probs = probs.clone()
incorrect_probs[..., correct_index] = -float("inf")
max_incorrect_probs, _ = incorrect_probs.max(dim=-1)
argmax_scores = probs.argmax(dim=-1)
within_delta = (max_incorrect_probs > correct_probs) & (
(max_incorrect_probs - correct_probs) <= delta
)
predicted_scores = torch.where(
within_delta,
torch.tensor(correct_index, device=scores_tensor.device),
argmax_scores,
)
else:
if delta is not None and (correct_index < 0 or correct_index >= num_classes):
logger.warning(
"Delta provided but correct_index=%s is out of range for %s classes.",
correct_index,
num_classes,
)
predicted_scores = torch.argmax(scores_tensor, dim=-1)
return predicted_scores
def run_inference(
audio_file_path: str,
transcript: str,
model: GOPPhonemeClassifier,
processor: Wav2Vec2Processor,
deltas: Dict[str, float] | None = None,
correct_index: int = 0,
):
if not audio_file_path or not transcript:
return "<p style='text-align:center; color:red;'>Please provide both an audio file and the transcript.</p>"
try:
waveform_np, original_sr = sf.read(audio_file_path, dtype="float32", always_2d=True)
waveform = torch.from_numpy(waveform_np.T)
duration_seconds = waveform.shape[1] / original_sr
if duration_seconds > MAX_AUDIO_DURATION_SECONDS:
raise ValueError(f"The audio recording should not be longer than {MAX_AUDIO_DURATION_SECONDS} seconds.")
if waveform.shape[0] > MONO_CHANNEL:
waveform = torch.mean(waveform, dim=0, keepdim=True)
if original_sr != SAMPLING_RATE:
resampler = torchaudio.transforms.Resample(orig_freq=original_sr, new_freq=SAMPLING_RATE)
waveform = resampler(waveform)
audio_input = waveform.squeeze(0)
processed_audio = processor(audio_input, sampling_rate=SAMPLING_RATE, return_tensors="pt", padding=True)
model_device = next(model.parameters()).device
input_values = processed_audio.input_values.to(model_device)
attention_mask = processed_audio.attention_mask.to(model_device)
canonical_token_ids, token_lengths, token_mask, display_tokens = _prepare_canonical_tokens(
transcript, processor, model_device
)
with torch.no_grad():
outputs = model(
input_values=input_values,
attention_mask=attention_mask,
canonical_token_ids=canonical_token_ids,
token_lengths=token_lengths,
token_mask=token_mask,
)
return _extract_head_predictions(
outputs.logits,
token_mask,
display_tokens,
deltas=deltas,
correct_index=correct_index,
)
except Exception as exc:
logger.error("An error occurred during inference: %s", exc, exc_info=True)
return f"<p style='text-align:center; color:red;'>An error occurred: {exc}</p>"
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