whisper-large-v3 模型

模型介绍

Whisper 是由 OpenAI 的 Alec Radford 等人提出的顶尖自动语音识别（ASR）与语音翻译模型，相关成果发表于论文《Robust Speech Recognition via Large-Scale Weak Supervision》。作为依托大规模弱监督训练的代表性模型，Whisper 凭借超过 500 万小时标注数据的训练基础，在零样本场景下展现出极强的泛化能力，能够适配多种数据集与应用领域，为语音处理任务提供高效解决方案。

在 Whisper 系列模型中，large-v3 版本是重要升级迭代产物，其核心架构与此前的 large、large-v2 版本保持一致，仅在细节上进行优化调整，具体包括两点：一是将 spectrogram（频谱图）输入的 Mel 频率 bin 数量从 80 提升至 128，优化对音频频率特征的捕捉；二是新增粤语语言令牌，进一步扩展对特定语言的支持能力。

训练数据与流程上，Whisper large-v3 采用混合数据集训练，包含 100 万小时弱标注音频，以及 400 万小时通过 Whisper large-v2 生成伪标签的音频，整体训练覆盖 2.0 个epoch。凭借更优质的训练数据与优化设计，large-v3 版本在多语言场景下性能显著提升，相较于 Whisper large-v2，其错误率降低 10% - 20%，成为当前语音识别与翻译领域的主流选择之一。

模型加载

python 复制代码

import torch
from modelscope import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset


device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "openai-mirror/whisper-large-v3"

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

模型结构

复制代码

Downloading Model from https://www.modelscope.cn to directory: /home/six/.cache/modelscope/hub/models/openai-mirror/whisper-large-v3
WhisperForConditionalGeneration(
  (model): WhisperModel(
    (encoder): WhisperEncoder(
      (conv1): Conv1d(128, 1280, kernel_size=(3,), stride=(1,), padding=(1,))
      (conv2): Conv1d(1280, 1280, kernel_size=(3,), stride=(2,), padding=(1,))
      (embed_positions): Embedding(1500, 1280)
      (layers): ModuleList(
        (0-31): 32 x WhisperEncoderLayer(
          (self_attn): WhisperAttention(
            (k_proj): Linear(in_features=1280, out_features=1280, bias=False)
            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)
            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)
            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)
          )
          (self_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
          (activation_fn): GELUActivation()
          (fc1): Linear(in_features=1280, out_features=5120, bias=True)
          (fc2): Linear(in_features=5120, out_features=1280, bias=True)
          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
        )
      )
      (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
    )
    (decoder): WhisperDecoder(
      (embed_tokens): Embedding(51866, 1280, padding_idx=50256)
      (embed_positions): WhisperPositionalEmbedding(448, 1280)
      (layers): ModuleList(
        (0-31): 32 x WhisperDecoderLayer(
          (self_attn): WhisperAttention(
            (k_proj): Linear(in_features=1280, out_features=1280, bias=False)
            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)
            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)
            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)
          )
          (activation_fn): GELUActivation()
          (self_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
          (encoder_attn): WhisperAttention(
            (k_proj): Linear(in_features=1280, out_features=1280, bias=False)
            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)
            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)
            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)
          )
          (encoder_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
          (fc1): Linear(in_features=1280, out_features=5120, bias=True)
          (fc2): Linear(in_features=5120, out_features=1280, bias=True)
          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
        )
      )
      (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
    )
  )
  (proj_out): Linear(in_features=1280, out_features=51866, bias=False)
)