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# Copyright (2023) Tsinghua University, Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import soundfile as sf
import torch.nn as nn
import torch.nn.functional as F
from peft import LoraConfig, TaskType, get_peft_model
from transformers import (
WhisperFeatureExtractor,
WhisperModel,
LlamaForCausalLM,
LlamaTokenizer
)
import librosa
from beats.BEATs import BEATsConfig, BEATs
from qformer.Qformer import BertConfig, BertLMHeadModel
class SALMONN(nn.Module):
def __init__(
self,
ckpt,
whisper_path,
beats_path,
vicuna_path,
speech_qformer_token_num=1,
speech_qformer_layer=2,
lora=True,
lora_alpha=32,
lora_rank=8,
lora_dropout=0.1,
second_per_frame=0.333333,
second_stride=0.333333,
low_resource=False
):
super().__init__()
# feature_extractor
self.feature_extractor = WhisperFeatureExtractor.from_pretrained(whisper_path)
# whisper
self.speech_encoder = WhisperModel.from_pretrained(whisper_path).encoder
self.ln_speech = nn.LayerNorm(self.speech_encoder.config.d_model)
# beats
self.beats_ckpt = beats_path
beats_checkpoint = torch.load(self.beats_ckpt, map_location='cpu')
beats_cfg = BEATsConfig(beats_checkpoint['cfg'])
beats = BEATs(beats_cfg)
beats.load_state_dict(beats_checkpoint['model'])
self.beats = beats
self.ln_audio = nn.LayerNorm(self.beats.cfg.encoder_embed_dim)
for name, param in self.beats.named_parameters():
param.requires_grad = False
self.beats.eval()
# init speech Qformer
self.speech_Qformer, self.speech_query_tokens = self.init_speech_Qformer(
speech_qformer_token_num,
self.speech_encoder.config.d_model + self.beats.cfg.encoder_embed_dim,
speech_qformer_layer,
)
self.second_per_frame = second_per_frame
self.second_stride = second_stride
# vicuna
if not low_resource:
self.llama_model = LlamaForCausalLM.from_pretrained(
vicuna_path,
torch_dtype=torch.float16,
)
else:
self.llama_model = LlamaForCausalLM.from_pretrained(
vicuna_path,
torch_dtype=torch.float16,
load_in_8bit=True,
device_map={'': 0}
)
# lora
self.lora = lora
if lora:
target_modules = None
self.peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=True,
r=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
target_modules=target_modules,
)
self.llama_model = get_peft_model(self.llama_model, self.peft_config)
# tokenizer
self.llama_tokenizer = LlamaTokenizer.from_pretrained(vicuna_path, use_fast=False)
self.llama_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
self.llama_tokenizer.padding_side = "right"
# proj
self.speech_llama_proj = nn.Linear(
self.speech_Qformer.config.hidden_size, self.llama_model.config.hidden_size)
# load ckpt
ckpt_dict = torch.load(ckpt)['model']
self.load_state_dict(ckpt_dict, strict=False)
def generate(
self,
wav_path,
prompt,
prompt_pattern="USER: <Speech><SpeechHere></Speech> {}\nASSISTANT:",
device='cuda:0',
max_length=220,
num_beams=4,
do_sample=True,
min_length=1,
top_p=0.9,
repetition_penalty=1.0,
length_penalty=1.0,
temperature=1.0,
):
# read wav
wav, sr = sf.read(wav_path)
if len(wav.shape) == 2:
wav = wav[:, 0]
if len(wav) > 30 * sr:
wav = wav[: 30 * sr]
if sr != 16000:
wav = librosa.resample(wav, orig_sr=sr, target_sr=16000, res_type="fft")
# whisper
spectrogram = self.feature_extractor(wav, return_tensors="pt", sampling_rate=16000).input_features.to(device) # [1, 80, 3000]
speech_embeds = self.speech_encoder(spectrogram, return_dict=True).last_hidden_state
# beats
raw_wav = torch.from_numpy(wav).to(device).unsqueeze(0)
audio_padding_mask = torch.zeros(raw_wav.shape, device=device).bool()
audio_embeds, _ = self.beats.extract_features(raw_wav, padding_mask=audio_padding_mask, feature_only=True)
# auditory embeds
speech_embeds = self.ln_speech(speech_embeds)
audio_embeds = self.ln_audio(audio_embeds)
audio_embeds = F.pad(audio_embeds, (0, 0, 0, speech_embeds.size(1) - audio_embeds.size(1)))
speech_embeds = torch.cat([speech_embeds, audio_embeds], dim=-1)
# split frames
B, T, C = speech_embeds.shape
kernel = round(T * self.second_per_frame / 30.0)
stride = round(T * self.second_stride / 30.0)
kernel = (1, kernel)
stride = (1, stride)
speech_embeds_tr = speech_embeds.transpose(1, 2).unsqueeze(2)
speech_embeds_overlap = F.unfold(speech_embeds_tr, kernel_size=kernel, dilation=1, padding=0, stride=stride)
_, _, L = speech_embeds_overlap.shape
speech_embeds_overlap = speech_embeds_overlap.view(B, -1, kernel[1], L)
speech_embeds_overlap = torch.permute(speech_embeds_overlap, [0, 3, 2, 1])
speech_embeds = speech_embeds_overlap.reshape(-1, kernel[1], C)
speech_atts = torch.ones(speech_embeds.size()[:-1], dtype=torch.long, device=speech_embeds.device)
# Qformer
query_tokens = self.speech_query_tokens.expand(speech_embeds.shape[0], -1, -1)
query_output = self.speech_Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=speech_embeds,
encoder_attention_mask=speech_atts,
return_dict=True,
)
speech_embeds = self.speech_llama_proj(query_output.last_hidden_state)
speech_embeds = speech_embeds.view(B, -1, speech_embeds.size(2)).contiguous()
speech_atts = torch.ones(speech_embeds.size()[:-1], dtype=torch.long).to(speech_embeds.device)
# USER: <Speech>speech_embeds<Speech> prompt\nASSISTANT:
embed_tokens = self.llama_model.model.model.embed_tokens if self.lora else self.llama_model.model.embed_tokens
prompt_left, prompts_right = prompt_pattern.format(prompt).split('<SpeechHere>')
prompt_left_ids = self.llama_tokenizer(
prompt_left,
return_tensors="pt",
add_special_tokens=False
).to(speech_embeds.device).input_ids
prompt_left_embeds = embed_tokens(prompt_left_ids)
prompt_right_ids = self.llama_tokenizer(
prompts_right,
return_tensors="pt",
add_special_tokens=False
).to(speech_embeds.device).input_ids
prompt_right_embeds = embed_tokens(prompt_right_ids)
bos_embeds = self.llama_model.model.embed_tokens(
torch.ones(
[1, 1],
dtype=torch.long,
device=device,
) * self.llama_tokenizer.bos_token_id
) if not self.lora else self.llama_model.model.model.embed_tokens(
torch.ones(
[1, 1],
dtype=torch.long,
device=device,
) * self.llama_tokenizer.bos_token_id
)
embeds = torch.cat([bos_embeds, prompt_left_embeds, speech_embeds, prompt_right_embeds], dim=1)
atts = torch.ones(embeds.size()[:-1], dtype=torch.long).to(embeds.device)
# generate
output = self.llama_model.generate(
inputs_embeds=embeds,
max_length=max_length,
num_beams=num_beams,
do_sample=do_sample,
min_length=min_length,
top_p=top_p,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
temperature=temperature,
attention_mask=atts,
bos_token_id=self.llama_tokenizer.bos_token_id,
eos_token_id=self.llama_tokenizer.eos_token_id,
pad_token_id=self.llama_tokenizer.pad_token_id
)
output_text = self.llama_tokenizer.batch_decode(output, add_special_tokens=False, skip_special_tokens=True)
return output_text
def init_speech_Qformer(self, num_query_token, speech_width, num_hidden_layers=2):
encoder_config = BertConfig()
encoder_config.num_hidden_layers = num_hidden_layers
encoder_config.encoder_width = speech_width
encoder_config.add_cross_attention = True
encoder_config.cross_attention_freq = 1
encoder_config.query_length = num_query_token
Qformer = BertLMHeadModel(config=encoder_config)
query_tokens = nn.Parameter(
torch.zeros(1, num_query_token, encoder_config.hidden_size)
)
query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
return Qformer, query_tokens
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