Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,936 Bytes
7c34c28 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
from typing import Dict, List, Optional
import torch
import torch.nn as nn
from transformers import AutoFeatureExtractor, WhisperModel
from multi_token.data_tools import load_audio
from multi_token.modalities.base_modality import Modality
from multi_token.modalities.projectors import (
build_mlp_vector_projector,
)
OUTPUT_EMB_SIZE = 768
class WhisperAudioModule(nn.Module):
def __init__(self, model_name_or_path: str):
super().__init__()
self.model_name_or_path = model_name_or_path
self.model = None
self.feature_extractor = None
self.load_model()
def load_model(self):
self.model = WhisperModel.from_pretrained(self.model_name_or_path)
self.feature_extractor = AutoFeatureExtractor.from_pretrained(
self.model_name_or_path
)
self.model.requires_grad_(False)
@torch.no_grad()
def forward(self, audios) -> torch.Tensor:
hidden_states = []
for i in range(audios.shape[0]):
decoder_input_ids = (
torch.tensor([[1]]) * self.model.config.decoder_start_token_id
)
last_hidden_state = self.model(
audios[i].to(device=self.device, dtype=self.dtype),
decoder_input_ids=decoder_input_ids.to(device=self.device),
).last_hidden_state
hidden_states.append(last_hidden_state)
last_hidden_state = torch.stack(hidden_states)
return last_hidden_state.view(-1, 1, OUTPUT_EMB_SIZE)
@property
def dtype(self):
return self.model.dtype
@property
def device(self):
return self.model.device
class WhisperAudioModality(Modality):
def __init__(
self,
model_name_or_path: str = "openai/whisper-small",
num_projector_layers: int = 2,
num_tokens_output: int = 10,
):
self.model_name_or_path = model_name_or_path
self.module = WhisperAudioModule(model_name_or_path=self.model_name_or_path)
self.num_projector_layers = num_projector_layers
self.num_tokens_output = num_tokens_output
def build_projector(self, lm_hidden_size: int) -> nn.Module:
return build_mlp_vector_projector(
input_hidden_size=OUTPUT_EMB_SIZE,
lm_hidden_size=lm_hidden_size,
num_layers=self.num_projector_layers,
num_tokens=self.num_tokens_output,
)
@property
def name(self) -> str:
return "audio_whisper"
@property
def token(self) -> str:
return "<speech>"
@property
def data_key(self) -> str:
return "speech_audios"
@property
def token_width(self) -> int:
return self.num_tokens_output
def to(self, dtype: torch.dtype, device: torch.device) -> "WhisperAudioModality":
self.module.to(dtype=dtype, device=device)
return self
def preprocess_rows(self, rows: List[Dict]) -> List[Optional[torch.Tensor]]:
row_values = []
for row in rows:
audios = []
for audio_dict in row[self.data_key]:
audio_dict = load_audio(
audio_dict,
target_sampling_rate=self.module.feature_extractor.sampling_rate,
)
audio_processed = self.module.feature_extractor(
audio_dict["array"],
return_tensors="pt",
sampling_rate=audio_dict["sampling_rate"],
).input_features
audios.append(audio_processed)
row_values.append(torch.stack(audios) if len(audios) > 0 else None)
return row_values
@torch.no_grad()
def forward(self, encoded_values: List[torch.Tensor]) -> List[torch.Tensor]:
audio_features = []
for audio_batch in encoded_values:
audio_features.append(self.module.forward(audio_batch))
return audio_features
|