mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-27 10:44:19 +00:00
431 lines
16 KiB
Python
Executable File
431 lines
16 KiB
Python
Executable File
import torch
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import torch.nn as nn
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import base64
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import gzip
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import warnings
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from tqdm import tqdm
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from dataclasses import dataclass
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from typing import Dict, Iterable, Optional, Union
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import os
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import urllib
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import hashlib
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import numpy as np
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import torch.nn.functional as F
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from torch import Tensor
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from whisper.decoding import decode as decode_function
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from whisper.transcribe import transcribe as transcribe_function
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@dataclass
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class ModelDimensions:
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n_mels: int
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n_audio_ctx: int
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n_audio_state: int
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n_audio_head: int
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n_audio_layer: int
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n_vocab: int
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n_text_ctx: int
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n_text_state: int
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n_text_head: int
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n_text_layer: int
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class LayerNorm(nn.LayerNorm):
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def forward(self, x: Tensor) -> Tensor:
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return super().forward(x.float()).type(x.dtype)
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class Linear(nn.Linear):
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def forward(self, x: Tensor) -> Tensor:
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return F.linear(
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x,
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self.weight.to(x.dtype),
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None if self.bias is None else self.bias.to(x.dtype),
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)
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class Conv1d(nn.Conv1d):
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def _conv_forward(
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self, x: Tensor, weight: Tensor, bias: Optional[Tensor]
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) -> Tensor:
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return super()._conv_forward(
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x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
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)
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def sinusoids(length, channels, max_timescale=10000):
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"""Returns sinusoids for positional embedding"""
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assert channels % 2 == 0
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log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
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inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
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scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
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return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
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class MultiHeadAttention(nn.Module):
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def __init__(self, n_state: int, n_head: int):
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super().__init__()
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self.n_head = n_head
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self.query = Linear(n_state, n_state)
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self.key = Linear(n_state, n_state, bias=False)
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self.value = Linear(n_state, n_state)
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self.out = Linear(n_state, n_state)
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def forward(
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self,
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x: Tensor,
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xa: Optional[Tensor] = None,
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mask: Optional[Tensor] = None,
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kv_cache: Optional[dict] = None,
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):
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q = self.query(x)
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if kv_cache is None or xa is None or self.key not in kv_cache:
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# hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
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# otherwise, perform key/value projections for self- or cross-attention as usual.
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k = self.key(x if xa is None else xa)
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v = self.value(x if xa is None else xa)
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else:
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# for cross-attention, calculate keys and values once and reuse in subsequent calls.
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k = kv_cache[self.key]
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v = kv_cache[self.value]
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wv, qk = self.qkv_attention(q, k, v, mask)
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return self.out(wv), qk
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def qkv_attention(
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self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None
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):
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n_batch, n_ctx, n_state = q.shape
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scale = (n_state // self.n_head) ** -0.25
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q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
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k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
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v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
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qk = q @ k
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if mask is not None:
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qk = qk + mask[:n_ctx, :n_ctx]
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qk = qk.float()
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w = F.softmax(qk, dim=-1).to(q.dtype)
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return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
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class ResidualAttentionBlock(nn.Module):
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def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
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super().__init__()
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self.attn = MultiHeadAttention(n_state, n_head)
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self.attn_ln = LayerNorm(n_state)
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self.cross_attn = (
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MultiHeadAttention(n_state, n_head) if cross_attention else None
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)
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self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
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n_mlp = n_state * 4
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self.mlp = nn.Sequential(
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Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)
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)
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self.mlp_ln = LayerNorm(n_state)
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def forward(
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self,
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x: Tensor,
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xa: Optional[Tensor] = None,
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mask: Optional[Tensor] = None,
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kv_cache: Optional[dict] = None,
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):
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x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
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if self.cross_attn:
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x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
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x = x + self.mlp(self.mlp_ln(x))
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return x
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class AudioEncoder(nn.Module):
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def __init__(
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self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int
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):
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super().__init__()
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self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
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self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
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self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
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self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
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[ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
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)
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self.ln_post = LayerNorm(n_state)
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def forward(self, x: Tensor):
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"""
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x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
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the mel spectrogram of the audio
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"""
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x = F.gelu(self.conv1(x))
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x = F.gelu(self.conv2(x))
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x = x.permute(0, 2, 1)
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# change whisper to process audio with any length
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x = (x + self.positional_embedding[:x.shape[1],:]).to(x.dtype)
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for block in self.blocks:
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x = block(x)
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x = self.ln_post(x)
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return x
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class TextDecoder(nn.Module):
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def __init__(
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self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int
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):
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super().__init__()
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self.token_embedding = nn.Embedding(n_vocab, n_state)
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self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
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self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
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[
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ResidualAttentionBlock(n_state, n_head, cross_attention=True)
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for _ in range(n_layer)
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]
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)
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self.ln = LayerNorm(n_state)
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mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
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self.register_buffer("mask", mask, persistent=False)
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def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None):
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"""
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x : torch.LongTensor, shape = (batch_size, <= n_ctx)
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the text tokens
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xa : torch.Tensor, shape = (batch_size, n_audio_ctx, n_audio_state)
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the encoded audio features to be attended on
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"""
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offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
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x = (
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self.token_embedding(x)
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+ self.positional_embedding[offset : offset + x.shape[-1]]
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)
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x = x.to(xa.dtype)
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for block in self.blocks:
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x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
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x = self.ln(x)
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logits = (
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x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)
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).float()
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return logits
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class Whisper(nn.Module):
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def __init__(self, dims: ModelDimensions):
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super().__init__()
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self.dims = dims
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self.encoder = AudioEncoder(
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self.dims.n_mels,
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self.dims.n_audio_ctx,
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self.dims.n_audio_state,
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self.dims.n_audio_head,
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self.dims.n_audio_layer,
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)
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self.decoder = TextDecoder(
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self.dims.n_vocab,
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self.dims.n_text_ctx,
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self.dims.n_text_state,
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self.dims.n_text_head,
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self.dims.n_text_layer,
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)
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# use the last half layers for alignment by default; see `set_alignment_heads()` below
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all_heads = torch.zeros(
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self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool
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)
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all_heads[self.dims.n_text_layer // 2 :] = True
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self.register_buffer("alignment_heads", all_heads.to_sparse(), persistent=False)
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def set_alignment_heads(self, dump: bytes):
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array = np.frombuffer(
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gzip.decompress(base64.b85decode(dump)), dtype=bool
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).copy()
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mask = torch.from_numpy(array).reshape(
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self.dims.n_text_layer, self.dims.n_text_head
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)
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self.register_buffer("alignment_heads", mask.to_sparse(), persistent=False)
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def embed_audio(self, mel: torch.Tensor):
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return self.encoder(mel)
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def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
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return self.decoder(tokens, audio_features)
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def forward(
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self, mel: torch.Tensor, tokens: torch.Tensor
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) -> Dict[str, torch.Tensor]:
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return self.decoder(tokens, self.encoder(mel))
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@property
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def device(self):
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return next(self.parameters()).device
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@property
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def is_multilingual(self):
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return self.dims.n_vocab >= 51865
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@property
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def num_languages(self):
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return self.dims.n_vocab - 51765 - int(self.is_multilingual)
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def install_kv_cache_hooks(self, cache: Optional[dict] = None):
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"""
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The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
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tensors calculated for the previous positions. This method returns a dictionary that stores
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all caches, and the necessary hooks for the key and value projection modules that save the
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intermediate tensors to be reused during later calculations.
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Returns
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-------
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cache : Dict[nn.Module, torch.Tensor]
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A dictionary object mapping the key/value projection modules to its cache
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hooks : List[RemovableHandle]
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List of PyTorch RemovableHandle objects to stop the hooks to be called
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"""
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cache = {**cache} if cache is not None else {}
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hooks = []
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def save_to_cache(module, _, output):
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if module not in cache or output.shape[1] > self.dims.n_text_ctx:
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# save as-is, for the first token or cross attention
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cache[module] = output
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else:
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cache[module] = torch.cat([cache[module], output], dim=1).detach()
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return cache[module]
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def install_hooks(layer: nn.Module):
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if isinstance(layer, MultiHeadAttention):
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hooks.append(layer.key.register_forward_hook(save_to_cache))
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hooks.append(layer.value.register_forward_hook(save_to_cache))
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self.decoder.apply(install_hooks)
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return cache, hooks
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transcribe = transcribe_function
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decode = decode_function
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_MODELS = {
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"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
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"tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
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"base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
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"base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
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"small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
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"small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
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"medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
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"medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
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"large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
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"large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
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"large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
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"large": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
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}
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def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
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os.makedirs(root, exist_ok=True)
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expected_sha256 = url.split("/")[-2]
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download_target = os.path.join(root, os.path.basename(url))
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if os.path.exists(download_target) and not os.path.isfile(download_target):
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raise RuntimeError(f"{download_target} exists and is not a regular file")
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if os.path.isfile(download_target):
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with open(download_target, "rb") as f:
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model_bytes = f.read()
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if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
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return model_bytes if in_memory else download_target
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else:
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warnings.warn(
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f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"
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)
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with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
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with tqdm(
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total=int(source.info().get("Content-Length")),
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ncols=80,
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unit="iB",
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unit_scale=True,
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unit_divisor=1024,
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) as loop:
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while True:
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buffer = source.read(8192)
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if not buffer:
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break
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output.write(buffer)
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loop.update(len(buffer))
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model_bytes = open(download_target, "rb").read()
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if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
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raise RuntimeError(
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"Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."
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)
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return model_bytes if in_memory else download_target
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def load_model(
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name: str,
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device: Optional[Union[str, torch.device]] = 'cpu',
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download_root: str = None,
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in_memory: bool = False,
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) -> Whisper:
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"""
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Load a Whisper ASR model
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Parameters
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----------
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name : str
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one of the official model names listed by `whisper.available_models()`, or
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path to a model checkpoint containing the model dimensions and the model state_dict.
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device : Union[str, torch.device]
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the PyTorch device to put the model into
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download_root: str
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path to download the model files; by default, it uses "~/.cache/whisper"
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in_memory: bool
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whether to preload the model weights into host memory
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Returns
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-------
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model : Whisper
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The Whisper ASR model instance
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"""
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# if device is None:
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# device = "cuda" if torch.cuda.is_available() else "cpu"
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if download_root is None:
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default = os.path.join(os.path.expanduser("~"), ".cache")
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download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper")
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if name in _MODELS:
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checkpoint_file = _download(_MODELS[name], download_root, in_memory)
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# alignment_heads = _ALIGNMENT_HEADS[name]
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alignment_heads = None
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elif os.path.isfile(name):
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checkpoint_file = open(name, "rb").read() if in_memory else name
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alignment_heads = None
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else:
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raise RuntimeError(
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f"Model {name} not found; available models = {available_models()}"
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)
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with (
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io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")
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) as fp:
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checkpoint = torch.load(fp, map_location=device)
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del checkpoint_file
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dims = ModelDimensions(**checkpoint["dims"])
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model = Whisper(dims)
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model.load_state_dict(checkpoint["model_state_dict"])
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return model.to(device) |