2025年多模态AI系统中的语音处理与跨模态转换技术

# 多模态语音处理模型示例实现
import torch
import torch.nn as nn
from transformers import CLIPModel, WhisperModel, GPT2LMHeadModel, UNet2DModel

class MultimodalSpeechProcessor(nn.Module):
    def __init__(self, clip_model_name, whisper_model_name, text_decoder_name, image_decoder_name):
        super().__init__()
        # 加载CLIP模型作为多模态编码器
        self.clip_model = CLIPModel.from_pretrained(clip_model_name)
        # 加载Whisper模型作为语音处理器
        self.whisper_model = WhisperModel.from_pretrained(whisper_model_name)
        # 加载文本解码器
        self.text_decoder = GPT2LMHeadModel.from_pretrained(text_decoder_name)
        # 加载图像解码器
        self.image_decoder = UNet2DModel(
            sample_size=256,
            in_channels=4,
            out_channels=3,
            layers_per_block=2,
            block_out_channels=(128, 128, 256, 256, 512, 512),
            down_block_types=(
                "DownBlock2D", "DownBlock2D", "DownBlock2D",
                "DownBlock2D", "DownBlock2D", "DownBlock2D"
            ),
            up_block_types=(
                "UpBlock2D", "UpBlock2D", "UpBlock2D",
                "UpBlock2D", "UpBlock2D", "UpBlock2D"
            ),
        )
        
        # 特征投影层
        self.speech_projection = nn.Linear(
            self.whisper_model.config.d_model,
            self.clip_model.config.projection_dim
        )
        self.text_projection = nn.Linear(
            self.clip_model.config.projection_dim,
            self.text_decoder.config.hidden_size
        )
        self.image_projection = nn.Linear(
            self.clip_model.config.projection_dim,
            4 * 256 * 256  # 映射到隐空间
        )
        
        # Dropout层用于防止过拟合
        self.dropout = nn.Dropout(0.3)
        
    def encode_speech(self, speech_inputs):
        # 使用Whisper编码语音
        outputs = self.whisper_model(speech_inputs)
        speech_features = outputs.last_hidden_state.mean(dim=1)  # 取时间维度的平均值
        # 投影到CLIP特征空间
        projected_features = self.speech_projection(speech_features)
        return projected_features
        
    def encode_text(self, text_inputs):
        # 使用CLIP编码文本
        text_features = self.clip_model.get_text_features(**text_inputs)
        return text_features
        
    def encode_image(self, image_inputs):
        # 使用CLIP编码图像
        image_features = self.clip_model.get_image_features(**image_inputs)
        return image_features
        
    def decode_to_text(self, features, max_length=100, temperature=1.0):
        # 将特征投影到文本解码器的隐藏空间
        projected_features = self.text_projection(features)
        projected_features = self.dropout(projected_features)
        
        # 初始化生成过程
        batch_size = projected_features.size(0)
        bos_token_id = 49406  # CLIPTokenizer的bos_token_id
        generated = torch.full((batch_size, 1), bos_token_id, dtype=torch.long, device=projected_features.device)
        
        # 生成文本
        for _ in range(max_length-1):
            decoder_outputs = self.text_decoder(input_ids=generated, encoder_hidden_states=projected_features.unsqueeze(1))
            next_token_logits = decoder_outputs.logits[:, -1, :]
            next_token_logits = next_token_logits / temperature
            next_token = torch.multinomial(torch.softmax(next_token_logits, dim=-1), num_samples=1)
            generated = torch.cat([generated, next_token], dim=1)
        
        return generated
        
    def decode_to_image(self, features, num_inference_steps=50):
        # 将特征投影到图像解码器的隐空间
        projected_features = self.image_projection(features)
        projected_features = self.dropout(projected_features)
        latent = projected_features.view(-1, 4, 256, 256)
        
        # 简单的扩散模型推理过程
        with torch.no_grad():
            for _ in range(num_inference_steps):
                noise_pred = self.image_decoder(sample=latent).sample
                latent = latent - 0.1 * noise_pred
        
        # 将隐空间转换为图像空间
        image = torch.sigmoid(latent) * 2 - 1
        
        return image

# 示例使用：语音到文本转换
# whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
# audio = ...  # 加载音频
# inputs = whisper_processor(audio, return_tensors="pt")
# speech_features = model.encode_speech(inputs.input_features)
# generated_text_ids = model.decode_to_text(speech_features)
# generated_text = whisper_processor.decode(generated_text_ids[0], skip_special_tokens=True)

# 语音识别核心功能实现
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration

class AdvancedSpeechRecognizer:
    def __init__(self, model_name="openai/whisper-large-v3", device=None):
        # 初始化设备
        self.device = device if device else ("cuda" if torch.cuda.is_available() else "cpu")
        # 加载处理器和模型
        self.processor = WhisperProcessor.from_pretrained(model_name)
        self.model = WhisperForConditionalGeneration.from_pretrained(model_name).to(self.device)
        # 设置强制使用的语言和任务
        self.forced_decoder_ids = self.processor.get_decoder_prompt_ids(language="chinese", task="transcribe")
        
    def load_speech_recognition_model(self, model_name=None):
        """加载指定的语音识别模型"""
        if model_name:
            self.processor = WhisperProcessor.from_pretrained(model_name)
            self.model = WhisperForConditionalGeneration.from_pretrained(model_name).to(self.device)
            self.forced_decoder_ids = self.processor.get_decoder_prompt_ids(language="chinese", task="transcribe")
        return self.model
        
    def transcribe_audio(self, audio_tensor, language="chinese", task="transcribe"):
        """转录音频为文本"""
        # 预处理音频
        inputs = self.processor(audio_tensor, return_tensors="pt", sampling_rate=16000).input_features.to(self.device)
        # 设置语言和任务
        forced_decoder_ids = self.processor.get_decoder_prompt_ids(language=language, task=task)
        # 生成转录结果
        with torch.no_grad():
            predicted_ids = self.model.generate(inputs, forced_decoder_ids=forced_decoder_ids)
        # 解码结果
        transcription = self.processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
        return transcription
        
    def transcribe_audio_with_timestamps(self, audio_tensor, language="chinese", task="transcribe"):
        """转录音频为文本并包含时间戳"""
        # 预处理音频
        inputs = self.processor(audio_tensor, return_tensors="pt", sampling_rate=16000).input_features.to(self.device)
        # 设置语言和任务
        forced_decoder_ids = self.processor.get_decoder_prompt_ids(language=language, task=task)
        # 生成带时间戳的转录结果
        with torch.no_grad():
            predicted_ids = self.model.generate(inputs, forced_decoder_ids=forced_decoder_ids, return_timestamps=True)
        # 解码结果
        transcription = self.processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
        return transcription

# 示例使用
# import soundfile as sf
# audio, sr = sf.read("audio.wav")
# if sr != 16000:
#     # 重采样到16kHz
#     import librosa
#     audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)
# recognizer = AdvancedSpeechRecognizer()
# text = recognizer.transcribe_audio(audio)
# print("转录结果:", text)

# 语音识别模型量化示例
import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor

# 加载模型
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")

# 动态量化
quantized_model = torch.quantization.quantize_dynamic(
    model,
    {torch.nn.Linear, torch.nn.Conv1d},
    dtype=torch.qint8
)

# 保存量化后的模型
quantized_model.save_pretrained("./whisper-large-v3-quantized")
processor.save_pretrained("./whisper-large-v3-quantized")

# 加载量化后的模型
loaded_model = WhisperForConditionalGeneration.from_pretrained("./whisper-large-v3-quantized")
loaded_processor = WhisperProcessor.from_pretrained("./whisper-large-v3-quantized")

# 验证模型性能
# audio = ...  # 加载音频
# inputs = loaded_processor(audio, return_tensors="pt", sampling_rate=16000).input_features
# with torch.no_grad():
#     predicted_ids = loaded_model.generate(inputs)
# transcription = loaded_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]

# 多模态模型激活函数优化示例
import torch
import torch.nn as nn
import torch.nn.functional as F

# 自定义激活函数 - Swish激活函数
class Swish(nn.Module):
    def forward(self, x):
        return x * torch.sigmoid(x)

# 自定义激活函数 - Mish激活函数
class Mish(nn.Module):
    def forward(self, x):
        return x * torch.tanh(F.softplus(x))

# 替换模型中的激活函数示例
def replace_activations(model, old_activation, new_activation):
    for name, module in list(model.named_children()):
        if isinstance(module, old_activation):
            setattr(model, name, new_activation())
        else:
            replace_activations(module, old_activation, new_activation)

# 示例用法
# model = MultimodalSpeechProcessor(...)
# 
# 将模型中的ReLU激活函数替换为Swish激活函数
# replace_activations(model, nn.ReLU, Swish)