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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Imports" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 1, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"from typing import Optional, Generator\n", | ||
"import math\n", | ||
"import random\n", | ||
"import itertools\n", | ||
"from pathlib import Path\n", | ||
"\n", | ||
"import numpy as np\n", | ||
"import torch\n", | ||
"from torch import Tensor\n", | ||
"from torch import nn\n", | ||
"import torch.nn.functional as F\n", | ||
"import torchinfo\n", | ||
"import whisper\n", | ||
"import IPython.display as ipd\n", | ||
"\n", | ||
"from transformers import GPT2TokenizerFast\n", | ||
"import model2\n", | ||
"import whisper" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Original model loading" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 2, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"ori = whisper.load_model(\"tiny\", device=\"cpu\")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Modified model loading" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 3, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"data": { | ||
"text/plain": [ | ||
"dict_keys(['dims', 'model_state_dict'])" | ||
] | ||
}, | ||
"metadata": {}, | ||
"output_type": "display_data" | ||
}, | ||
{ | ||
"data": { | ||
"text/plain": [ | ||
"{'n_mels': 80,\n", | ||
" 'n_vocab': 51865,\n", | ||
" 'n_audio_ctx': 1500,\n", | ||
" 'n_audio_state': 384,\n", | ||
" 'n_audio_head': 6,\n", | ||
" 'n_audio_layer': 4,\n", | ||
" 'n_text_ctx': 448,\n", | ||
" 'n_text_state': 384,\n", | ||
" 'n_text_head': 6,\n", | ||
" 'n_text_layer': 4}" | ||
] | ||
}, | ||
"metadata": {}, | ||
"output_type": "display_data" | ||
} | ||
], | ||
"source": [ | ||
"tiny_path = Path(\"~/.cache/whisper/tiny.pt\").expanduser()\n", | ||
"with open(tiny_path, \"rb\") as f:\n", | ||
" checkpoint = torch.load(f)\n", | ||
"ipd.display(checkpoint.keys())\n", | ||
"ipd.display(checkpoint[\"dims\"])" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 4, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"model_dims = model2.ModelDimensions(**checkpoint[\"dims\"])\n", | ||
"modded = model2.Whisper(model_dims).eval()\n", | ||
"modded.load_state_dict(checkpoint[\"model_state_dict\"])\n", | ||
"scripted = torch.jit.script(modded)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Simple forward test" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 5, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"audio = whisper.load_audio(\"tests/jfk.flac\")\n", | ||
"audio = whisper.pad_or_trim(audio)\n", | ||
"\n", | ||
"mel = whisper.log_mel_spectrogram(audio).unsqueeze(0)\n", | ||
"\n", | ||
"# # detect the spoken language\n", | ||
"# _, probs = model.detect_language(mel)\n", | ||
"# print(f\"Detected language: {max(probs, key=probs.get)}\")\n", | ||
"\n", | ||
"# # decode the audio\n", | ||
"# options = whisper.DecodingOptions()\n", | ||
"# result = whisper.decode(model, mel, options)\n", | ||
"\n", | ||
"# # print the recognized text\n", | ||
"# print(result.text)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 6, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"True\n", | ||
"True\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"ori_encoded = ori.encoder(mel)\n", | ||
"modded_encoded = modded.encoder(mel)\n", | ||
"scripted_encoded = scripted.encoder(mel)\n", | ||
"\n", | ||
"print(torch.allclose(ori_encoded, modded_encoded))\n", | ||
"print(torch.allclose(ori_encoded, scripted_encoded))" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 7, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# TODO add decoder greedy decoding test" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"# Scratchpad" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 8, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"class Dummy(nn.Module):\n", | ||
" \n", | ||
" def __init__(self, keygen: Generator) -> None:\n", | ||
" super().__init__()\n", | ||
" self.unique_num = next(keygen)\n", | ||
" self.unique_num = next(keygen)\n", | ||
" self.lin = nn.Linear(4, 4)\n", | ||
" \n", | ||
" def forward(self, x: Tensor, cache: dict[int, Tensor]):\n", | ||
" if self.unique_num not in cache:\n", | ||
" cache[self.unique_num] = self.lin(x)\n", | ||
" return cache[self.unique_num]\n", | ||
" \n", | ||
" @torch.jit.export\n", | ||
" def generate(self, x: Tensor):\n", | ||
" print(self.unique_num)\n", | ||
" cache: dict[int, Tensor] = {}\n", | ||
" a = self.forward(x, cache)\n", | ||
" b = self.forward(x*2, cache)\n", | ||
" return a-b\n", | ||
"\n", | ||
"keygen = itertools.count()\n", | ||
"dummy = Dummy(keygen)\n", | ||
"sdummy = torch.jit.script(dummy)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 9, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"1\n" | ||
] | ||
}, | ||
{ | ||
"data": { | ||
"text/plain": [ | ||
"tensor([[0., 0., 0., 0.],\n", | ||
" [0., 0., 0., 0.],\n", | ||
" [0., 0., 0., 0.]], grad_fn=<SubBackward0>)" | ||
] | ||
}, | ||
"execution_count": 9, | ||
"metadata": {}, | ||
"output_type": "execute_result" | ||
} | ||
], | ||
"source": [ | ||
"dummy.generate(torch.randn(3, 4))" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 10, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"1\n" | ||
] | ||
}, | ||
{ | ||
"data": { | ||
"text/plain": [ | ||
"tensor([[0., 0., 0., 0.],\n", | ||
" [0., 0., 0., 0.],\n", | ||
" [0., 0., 0., 0.]], grad_fn=<SubBackward0>)" | ||
] | ||
}, | ||
"execution_count": 10, | ||
"metadata": {}, | ||
"output_type": "execute_result" | ||
} | ||
], | ||
"source": [ | ||
"sdummy.generate(torch.randn(3, 4))" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3.9.13 ('ml')", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.9.13" | ||
}, | ||
"orig_nbformat": 4, | ||
"vscode": { | ||
"interpreter": { | ||
"hash": "c788065d0627783e03f01588c616fdc081ccf79059243dc851e48ed3fc07eef9" | ||
} | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 2 | ||
} |