mirror of
https://github.com/openai/whisper.git
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636 lines
17 KiB
Plaintext
Generated
636 lines
17 KiB
Plaintext
Generated
{
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"cells": [
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"cell_type": "markdown",
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"metadata": {
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"id": "v5hvo8QWN-a9"
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},
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"source": [
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"# Installing Whisper\n",
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"\n",
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"The commands below will install the Python packages needed to use Whisper models and evaluate the transcription results."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"id": "ZsJUxc0aRsAf"
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},
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"source": [
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"! pip install git+https://github.com/openai/whisper.git\n",
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"! pip install jiwer"
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],
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "1IMEkgyagYto"
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},
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"source": [
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"# Loading the LibriSpeech dataset\n",
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"\n",
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"The following will load the test-clean split of the LibriSpeech corpus using torchaudio."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "3CqtR2Fi5-vP"
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},
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"source": [
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"import os\n",
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"import numpy as np\n",
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"\n",
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"try:\n",
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" import tensorflow # required in Colab to avoid protobuf compatibility issues\n",
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"except ImportError:\n",
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" pass\n",
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"\n",
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"import torch\n",
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"import pandas as pd\n",
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"import whisper\n",
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"import torchaudio\n",
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"\n",
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"from tqdm.notebook import tqdm\n",
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"\n",
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"\n",
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"DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
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],
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "GuCCB2KYOJCE"
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},
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"source": [
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"class LibriSpeech(torch.utils.data.Dataset):\n",
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" \"\"\"\n",
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" A simple class to wrap LibriSpeech and trim/pad the audio to 30 seconds.\n",
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" It will drop the last few seconds of a very small portion of the utterances.\n",
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" \"\"\"\n",
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" def __init__(self, split=\"test-clean\", device=DEVICE):\n",
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" self.dataset = torchaudio.datasets.LIBRISPEECH(\n",
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" root=os.path.expanduser(\"~/.cache\"),\n",
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" url=split,\n",
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" download=True,\n",
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" )\n",
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" self.device = device\n",
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"\n",
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" def __len__(self):\n",
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" return len(self.dataset)\n",
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"\n",
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" def __getitem__(self, item):\n",
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" audio, sample_rate, text, _, _, _ = self.dataset[item]\n",
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" assert sample_rate == 16000\n",
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" audio = whisper.pad_or_trim(audio.flatten()).to(self.device)\n",
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" mel = whisper.log_mel_spectrogram(audio)\n",
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" \n",
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" return (mel, text)"
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],
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"id": "-YcRU5jqNqo2"
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},
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"source": [
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"dataset = LibriSpeech(\"test-clean\")\n",
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"loader = torch.utils.data.DataLoader(dataset, batch_size=16)"
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],
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "0ljocCNuUAde"
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},
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"source": [
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"# Running inference on the dataset using a base Whisper model\n",
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"\n",
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"The following will take a few minutes to transcribe all utterances in the dataset."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "_PokfNJtOYNu",
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"outputId": "2c53ec44-bc93-4107-b4fa-214e3f71fe8e"
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},
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"source": [
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"model = whisper.load_model(\"base.en\")\n",
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"print(\n",
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" f\"Model is {'multilingual' if model.is_multilingual else 'English-only'} \"\n",
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" f\"and has {sum(np.prod(p.shape) for p in model.parameters()):,} parameters.\"\n",
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")"
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],
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"source": [
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"# predict without timestamps for short-form transcription\n",
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"options = whisper.DecodingOptions(language=\"en\", without_timestamps=True)"
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],
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"outputs": []
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},
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"height": 49,
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"referenced_widgets": [
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"outputId": "a813a792-3c91-4144-f11f-054fd6778023"
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"source": [
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"hypotheses = []\n",
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"references = []\n",
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"\n",
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"for mels, texts in tqdm(loader):\n",
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" results = model.decode(mels, options)\n",
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" hypotheses.extend([result.text for result in results])\n",
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" references.extend(texts)"
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],
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 424
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},
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"id": "4nTyynELQ42j",
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"outputId": "1c72d25a-3e87-4c60-a8d1-1da9d2f73bd7"
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},
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"source": [
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"data = pd.DataFrame(dict(hypothesis=hypotheses, reference=references))\n",
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"data"
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],
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "HPppEJRXX4ox"
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},
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"source": [
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"# Calculating the word error rate\n",
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"\n",
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"Now, we use our English normalizer implementation to standardize the transcription and calculate the WER."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"id": "dl-KBDflMhrg"
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},
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"source": [
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"import jiwer\n",
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"from whisper.normalizers import EnglishTextNormalizer\n",
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"\n",
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"normalizer = EnglishTextNormalizer()"
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],
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 641
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"outputId": "f2089bc9-f535-441e-f192-26e52ae82b5e"
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},
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"source": [
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"data[\"hypothesis_clean\"] = [normalizer(text) for text in data[\"hypothesis\"]]\n",
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"data[\"reference_clean\"] = [normalizer(text) for text in data[\"reference\"]]\n",
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"data"
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],
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"source": [
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"wer = jiwer.wer(list(data[\"reference_clean\"]), list(data[\"hypothesis_clean\"]))\n",
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"\n",
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"print(f\"WER: {wer * 100:.2f} %\")"
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],
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"outputs": []
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}
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],
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