6669 lines
202 KiB
Plaintext
6669 lines
202 KiB
Plaintext
{
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"cells": [
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{
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||
"cell_type": "markdown",
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||
"id": "cf966af3-94bf-46eb-93af-1995c326bc25",
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||
"metadata": {
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||
"id": "cf966af3-94bf-46eb-93af-1995c326bc25"
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||
},
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"source": [
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"# Wav2vec 2.0 With Open Brazilian Portuguese Datasets v2"
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]
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},
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{
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"cell_type": "markdown",
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||
"id": "78e2333a-f973-4483-8790-6cbd52ee7b9e",
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||
"metadata": {
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||
"id": "78e2333a-f973-4483-8790-6cbd52ee7b9e"
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},
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"source": [
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"This a the demonstration of a fine-tuned Wav2vec model for Brazilian Portuguese using the following datasets:\n",
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"\n",
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"- [CETUC](http://www02.smt.ufrj.br/~igor.quintanilha/alcaim.tar.gz): contains approximately 145 hours of Brazilian Portuguese speech distributed among 50 male and 50 female speakers, each pronouncing approximately 1,000 phonetically balanced sentences selected from the [CETEN-Folha](https://www.linguateca.pt/cetenfolha/) corpus.\n",
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"- [Multilingual Librispeech (MLS)](https://arxiv.org/abs/2012.03411): a massive dataset available in many languages. The MLS is based on audiobook recordings in public domain like [LibriVox](https://librivox.org/). The dataset contains a total of 6k hours of transcribed data in many languages. The set in Portuguese [used in this work](http://www.openslr.org/94/) (mostly Brazilian variant) has approximately 284 hours of speech, obtained from 55 audiobooks read by 62 speakers.\n",
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"- [VoxForge](http://www.voxforge.org/): is a project with the goal to build open datasets for acoustic models. The corpus contains approximately 100 speakers and 4,130 utterances of Brazilian Portuguese, with sample rates varying from 16kHz to 44.1kHz.\n",
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"- [Common Voice 6.1](https://commonvoice.mozilla.org/pt): is a project proposed by Mozilla Foundation with the goal to create a wide open dataset in different languages to train ASR models. In this project, volunteers donate and validate speech using the [oficial site](https://commonvoice.mozilla.org/pt). The set in Portuguese (mostly Brazilian variant) used in this work is the 6.1 version (pt_63h_2020-12-11) that contains about 50 validated hours and 1,120 unique speakers.\n",
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"- [Lapsbm](https://github.com/falabrasil/gitlab-resources): \"Falabrasil - UFPA\" is a dataset used by the Fala Brasil group to benchmark ASR systems in Brazilian Portuguese. Contains 35 speakers (10 females), each one pronouncing 20 unique sentences, totalling 700 utterances in Brazilian Portuguese. The audios were recorded in 22.05 kHz without environment control.\n",
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"\n",
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"These datasets were combined to build a larger Brazilian Portuguese dataset. All data was used for training except Common Voice dev/test sets, that were used for validation/test respectively.\n",
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"\n",
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"The original model was fine-tuned using [fairseq](https://github.com/pytorch/fairseq). This notebook uses a converted version of the original one.\n",
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"\n",
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"__NOTE: The common voice test reports 10% of WER, however, this model was trained using all the validated instances of Common Voice, except the instances of the test set. This means that some speakers of the train set can be present on the test set.__"
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]
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},
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{
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"cell_type": "markdown",
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||
"id": "ad5dea33-ccd9-44b6-b0fc-76853e672d63",
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"metadata": {
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"id": "ad5dea33-ccd9-44b6-b0fc-76853e672d63"
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},
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"source": [
|
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"## Imports and dependencies"
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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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||
"id": "d08fb56e-97d1-4095-bb60-2a15f3db4f2b",
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"metadata": {
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"id": "d08fb56e-97d1-4095-bb60-2a15f3db4f2b"
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},
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"outputs": [],
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"source": [
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"%%capture\n",
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"!pip install datasets\n",
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"!pip install jiwer\n",
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"!pip install torchaudio\n",
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"!pip install transformers\n",
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"!pip install soundfile"
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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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||
"id": "19ef3f04-a137-461a-bcdb-b1e9cd39639f",
|
||
"metadata": {
|
||
"id": "19ef3f04-a137-461a-bcdb-b1e9cd39639f"
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},
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||
"outputs": [],
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||
"source": [
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"import torchaudio\n",
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"from datasets import load_dataset, load_metric\n",
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"from transformers import (\n",
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" Wav2Vec2ForCTC,\n",
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" Wav2Vec2Processor,\n",
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")\n",
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"import torch\n",
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"import re\n",
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"import sys"
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]
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},
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{
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"cell_type": "markdown",
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||
"id": "bc38406f-a3c6-4e23-b8cf-0a1f21458a98",
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||
"metadata": {
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||
"id": "bc38406f-a3c6-4e23-b8cf-0a1f21458a98"
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},
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"source": [
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"## Preparation"
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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": 3,
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||
"id": "ce08659b-77c7-4886-b6ac-8b65e1637da8",
|
||
"metadata": {
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||
"id": "ce08659b-77c7-4886-b6ac-8b65e1637da8"
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},
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"outputs": [],
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"source": [
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"chars_to_ignore_regex = '[\\,\\?\\.\\!\\;\\:\\\"]' # noqa: W605\n",
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"wer = load_metric(\"wer\")\n",
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"device = \"cuda\""
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]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 5,
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||
"id": "899c51a2-a053-437e-a90b-d3fef7e3bed1",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 226,
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"id": "899c51a2-a053-437e-a90b-d3fef7e3bed1",
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"outputId": "b3d7c23d-1539-4ddf-b0bf-79aa8d5465e3"
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},
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"outputs": [
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||
{
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||
"name": "stderr",
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"output_type": "stream",
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||
"text": [
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||
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model_name = 'lgris/wav2vec2-large-xlsr-open-brazilian-portuguese-v2'\n",
|
||
"model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)\n",
|
||
"processor = Wav2Vec2Processor.from_pretrained(model_name)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "74632ea5-7ee1-4a0f-8cb9-90b97c7f4238",
|
||
"metadata": {
|
||
"id": "74632ea5-7ee1-4a0f-8cb9-90b97c7f4238"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def map_to_pred(batch):\n",
|
||
" features = processor(batch[\"speech\"], sampling_rate=batch[\"sampling_rate\"][0], padding=True, return_tensors=\"pt\")\n",
|
||
" input_values = features.input_values.to(device)\n",
|
||
" attention_mask = features.attention_mask.to(device)\n",
|
||
" with torch.no_grad():\n",
|
||
" logits = model(input_values, attention_mask=attention_mask).logits\n",
|
||
" pred_ids = torch.argmax(logits, dim=-1)\n",
|
||
" batch[\"predicted\"] = processor.batch_decode(pred_ids)\n",
|
||
" batch[\"predicted\"] = [pred.lower() for pred in batch[\"predicted\"]]\n",
|
||
" batch[\"target\"] = batch[\"sentence\"]\n",
|
||
" return batch"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1a711XsKtjTq",
|
||
"metadata": {
|
||
"id": "1a711XsKtjTq"
|
||
},
|
||
"source": [
|
||
"## Tests"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c0eded73-ac1a-4d0c-acde-0c99e9eff17c",
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||
"metadata": {
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||
"id": "c0eded73-ac1a-4d0c-acde-0c99e9eff17c"
|
||
},
|
||
"source": [
|
||
"### Test against Common Voice (In-domain)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
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||
"id": "79e0cf33-600a-4760-b15e-ff112c0a066a",
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||
"metadata": {
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"colab": {
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"height": 184,
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"0eda02341ae54ce7a8f30b3711031e75",
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"3ad5c714728142339a8abb73c59488ef",
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"0152261caea844938e951340d80452bf"
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|
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"id": "79e0cf33-600a-4760-b15e-ff112c0a066a",
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"outputId": "d13eaa31-515c-4ac4-f89d-e612805bd60b"
|
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},
|
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"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Using custom data configuration pt-ac779bf2c9f7c09b\n",
|
||
"Reusing dataset common_voice (/home/lucas/.cache/huggingface/datasets/common_voice/pt-ac779bf2c9f7c09b/6.1.0/078d412587e9efeb0ae2e574da99c31e18844c496008d53dc5c60f4159ed639b)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dataset = load_dataset(\"common_voice\", \"pt\", split=\"test\", data_dir=\"./cv-corpus-6.1-2020-12-11\")\n",
|
||
"\n",
|
||
"resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)\n",
|
||
"\n",
|
||
"def map_to_array(batch):\n",
|
||
" speech, _ = torchaudio.load(batch[\"path\"])\n",
|
||
" batch[\"speech\"] = resampler.forward(speech.squeeze(0)).numpy()\n",
|
||
" batch[\"sampling_rate\"] = resampler.new_freq\n",
|
||
" batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower().replace(\"’\", \"'\")\n",
|
||
" return batch"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "ece76299-555f-4f19-952a-5482e87a4427",
|
||
"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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|
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" 0%| | 0/4641 [00:00<?, ?ex/s]"
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|
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||
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|
||
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|
||
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|
||
"text": [
|
||
"/home/lucas/anaconda3/envs/wav2vec-huggingface/lib/python3.9/site-packages/torch/_tensor.py:575: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.\n",
|
||
"To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at /pytorch/aten/src/ATen/native/BinaryOps.cpp:467.)\n",
|
||
" return torch.floor_divide(self, other)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0.10693731749496782\n",
|
||
"nem u varamin os outros infumindoos de teterno um bombederste | nem o radar nem os outros instrumentos detectaram o bombardeiro stealth\n",
|
||
"edir dinheiro emprestado as pessoas da aldeia | pedir dinheiro emprestado às pessoas da aldeia\n",
|
||
"oito | oito\n",
|
||
"trão calcos | trancá-los\n",
|
||
"realizaram a investigação para resolver o problema | realizar uma investigação para resolver o problema\n",
|
||
"otube ainda é a melhor plataforma de vídeos | o youtube ainda é a melhor plataforma de vídeos\n",
|
||
"menina e menino beijando nas sombras | menina e menino beijando nas sombras\n",
|
||
"eu sou o senhor | eu sou o senhor\n",
|
||
"duas méticas sentam-se para abaixo de randes jornais | duas mulheres que sentam-se para baixo lendo jornais\n",
|
||
"eu originalmente esperava | eu originalmente esperava\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"ds = dataset.map(map_to_array)\n",
|
||
"result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))\n",
|
||
"print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\n",
|
||
"for pred, target in zip(result[\"predicted\"][:10], result[\"target\"][:10]):\n",
|
||
" print(pred, \"|\", target) "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "joOUfrfr6eoO",
|
||
"metadata": {
|
||
"id": "joOUfrfr6eoO"
|
||
},
|
||
"source": [
|
||
"**Result**: 10.69%"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cfffe14c-cfe3-4e51-af88-e6506a251555",
|
||
"metadata": {
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||
"id": "cfffe14c-cfe3-4e51-af88-e6506a251555"
|
||
},
|
||
"source": [
|
||
"### Test against [TEDx](http://www.openslr.org/100/) (Out-of-domain)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "9108777f-e70e-46c2-a9be-756c70dc5997",
|
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"metadata": {
|
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"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
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|
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|
||
"outputId": "a776d671-ab84-453f-88c1-14f4ecf63001"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/bin/bash: gdown: comando não encontrado\n",
|
||
"tar: tedx.tar.gz: Função open falhou: Arquivo ou diretório inexistente\n",
|
||
"tar: Error is not recoverable: exiting now\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!gdown --id 1HJEnvthaGYwcV_whHEywgH2daIN4bQna\n",
|
||
"!tar -xf tedx.tar.gz"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
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"id": "9c7837ca-e8ff-4b6e-90e5-f2101929d0d6",
|
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"metadata": {
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|
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|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Using custom data configuration default-e7254893d1ce52c3\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Downloading and preparing dataset csv/default (download: Unknown size, generated: Unknown size, post-processed: Unknown size, total: Unknown size) to /home/lucas/.cache/huggingface/datasets/csv/default-e7254893d1ce52c3/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23...\n"
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{
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|
||
"version_major": 2,
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"version_minor": 0
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||
},
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||
"text/plain": [
|
||
"0 tables [00:00, ? tables/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Dataset csv downloaded and prepared to /home/lucas/.cache/huggingface/datasets/csv/default-e7254893d1ce52c3/0.0.0/e138af468cb14e747fb46a19c787ffcfa5170c821476d20d5304287ce12bbc23. Subsequent calls will reuse this data.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dataset = load_dataset('csv', data_files={'test': 'test.csv'})['test']\n",
|
||
"\n",
|
||
"def map_to_array(batch):\n",
|
||
" speech, _ = torchaudio.load(batch[\"path\"])\n",
|
||
" batch[\"speech\"] = speech.squeeze(0).numpy()\n",
|
||
" batch[\"sampling_rate\"] = resampler.new_freq\n",
|
||
" batch[\"sentence\"] = re.sub(chars_to_ignore_regex, '', batch[\"sentence\"]).lower().replace(\"’\", \"'\")\n",
|
||
" return batch"
|
||
]
|
||
},
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|
||
" 0%| | 0/1020 [00:00<?, ?ex/s]"
|
||
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|
||
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|
||
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||
"metadata": {},
|
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"output_type": "display_data"
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||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"0.3453631795340338\n",
|
||
"com isso a gente vê que essa rede de pactuação de de diparcerias nos remete a um raciocínio lógico que ao que a gente crê que é a prevenção | com isso a gente vê que essa rede de pactuação de parcerias nos remete a um raciocínio lógico que é o que a gente crê que é a prevenção\n",
|
||
"ente vai para o resultado | e aí a gente vai pro resultado\n",
|
||
"curiosidade há o que eu descobri desde que comecei a fazer pesquisa lá no ensino médio | e a curiosidade é algo que descobri desde que comecei a fazer pesquisa lá no ensino médio\n",
|
||
"va de es qamicho | há vários caminhos\n",
|
||
"que é uma obsissão por comer solado | que é uma obsessão por comer saudável\n",
|
||
"isso é tão forte algolotão universal que existem dados que mostram que setenta e cinco por cento das reuniões são dominadas pela voz masculina | e isso é tão forte é algo tão universal que existem dados que mostram que das reuniões são dominadas pela voz masculina\n",
|
||
"não era exatamente isso não estávamos eveto | e não era exatamente isso que nós estávamos a ver\n",
|
||
"durante itoro médio ofiz pesquisa e estudei numa escola que chama a fundação liberate ficava relativamente próximo daqui | durante o ensino médio eu fiz pesquisa estudei numa escola que se chama fundação liberato que fica relativamente próxima daqui\n",
|
||
"oito anos atrás eu fui apresentado por uma doença que até então eu não conhecia e que é bem provável que a maior parte de nós todos aqui não conheçemos | oito anos atrás fui apresentado para uma doença que até então eu não conhecia e que é bem provável que a maior parte de nós todos aqui não conheçamos\n",
|
||
"o terceiro oé o museu do ripiope | o terceiro é o museu do hip hop\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"ds = dataset.map(map_to_array)\n",
|
||
"result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))\n",
|
||
"print(wer.compute(predictions=result[\"predicted\"], references=result[\"target\"]))\n",
|
||
"for pred, target in zip(result[\"predicted\"][:10], result[\"target\"][:10]):\n",
|
||
" print(pred, \"|\", target) "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "Ms9vtSCF6l9q",
|
||
"metadata": {
|
||
"id": "Ms9vtSCF6l9q"
|
||
},
|
||
"source": [
|
||
"**Result**: 34.53%"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"accelerator": "GPU",
|
||
"colab": {
|
||
"name": "wav2vec-brazilian-portuguese.ipynb",
|
||
"provenance": [],
|
||
"toc_visible": true
|
||
},
|
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|
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|
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|
||
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|
||
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|
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|
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