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from transformers import AutoTokenizer, AutoModelForSequenceClassification
from huggingface_hub import PyTorchModelHubMixin
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Union
from FFNN import FFNN
class ArtistCoherencyModel(nn.Module, PyTorchModelHubMixin):
def __init__(self, config: dict):
super().__init__()
coherency_model_repo_id = config["coherency_model_repo_id"]
artist_model_repo_id = config["artist_model_repo_id"]
ffnn_model_repo_id = config["ffnn_model_repo_id"]
self.coherency_model_tokenizer = AutoTokenizer.from_pretrained(
coherency_model_repo_id
)
self.artist_model_tokenizer = AutoTokenizer.from_pretrained(
artist_model_repo_id
)
self.coherency_model = AutoModelForSequenceClassification.from_pretrained(
coherency_model_repo_id
)
self.artist_model = AutoModelForSequenceClassification.from_pretrained(
artist_model_repo_id
)
self.ffnn = FFNN.from_pretrained(ffnn_model_repo_id)
def generate_artist_logits(self, song: str) -> torch.FloatTensor:
inputs = self.artist_model_tokenizer(
song, return_tensors="pt", max_length=512, truncation=True
)
with torch.no_grad():
return self.artist_model(**inputs).logits
def predict_artist(self, song: str) -> tuple[str, float]:
logits = F.softmax(self.generate_artist_logits(song)[0], dim=0)
predicted_class_id = logits.argmax().item()
return self.artist_model.config.id2label[predicted_class_id], 100 * float(
logits[predicted_class_id]
)
def generate_coherency_logits(self, song: str) -> torch.FloatTensor:
inputs = self.coherency_model_tokenizer(
song, return_tensors="pt", max_length=512, truncation=True
)
with torch.no_grad():
return self.coherency_model(**inputs).logits
def predict_coherency(self, song: str) -> tuple[str, float]:
logits = F.softmax(self.generate_coherency_logits(song)[0], dim=0)
predicted_class_id = logits.argmax().item()
return self.coherency_model.config.id2label[predicted_class_id], 100 * float(
logits[predicted_class_id]
)
def generate_song_embedding(self, song: str) -> torch.FloatTensor:
with torch.no_grad():
artist_logits = self.generate_artist_logits(song)
coherency_logits = self.generate_coherency_logits(song)
return torch.hstack((artist_logits[0], coherency_logits[0]))
def forward(self, song_or_embedding: Union[str, torch.Tensor]):
if type(song_or_embedding) is str:
song_or_embedding = self.generate_song_embedding(song_or_embedding)
return self.ffnn(song_or_embedding)
def generate_artist_coherency_logits(
self, song_or_embedding: Union[str, torch.Tensor]
) -> torch.FloatTensor:
with torch.no_grad():
return self.forward(song_or_embedding)
def generate_artist_coherency_score(
self, artist_name: str, song_or_embedding: Union[str, torch.Tensor]
) -> float:
coherent_index = self.ffnn.label2id[f"{artist_name}-coherent"]
logits = self.generate_artist_coherency_logits(song_or_embedding)
coherent_score = logits[coherent_index]
return 100 * float(coherent_score)
def predict(
self, song_or_embedding: Union[str, torch.Tensor], return_ids: bool = False
) -> Union[list[str], torch.Tensor]:
if type(song_or_embedding) is str:
song_or_embedding = self.generate_song_embedding(song_or_embedding)
return self.ffnn.predict(song_or_embedding, return_ids=return_ids)
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