Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers

New (11/2021): This blog post has been updated to feature XLSR’s
successor, called XLS-R.

Wav2Vec2 is a pretrained model for Automatic Speech Recognition
(ASR) and was released in September
2020
by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the
superior performance of Wav2Vec2 was demonstrated on one of the most
popular English datasets for ASR, called
LibriSpeech,
Facebook AI presented a multi-lingual version of Wav2Vec2, called
XLSR. XLSR stands for cross-lingual
speech representations and refers to model’s ability to learn speech
representations that are useful across multiple languages.

XLSR’s successor, simply called XLS-R (refering to the
”XLM-R
for Speech”), was released in November 2021 by Arun
Babu, Changhan Wang, Andros Tjandra, et al. XLS-R used almost half a
million hours of audio data in 128 languages for self-supervised
pre-training and comes in sizes ranging from 300 milion up to two
billion parameters. You can find the pretrained checkpoints on the 🤗
Hub:

Similar to BERT’s masked language modeling
objective, XLS-R learns
contextualized speech representations by randomly masking feature
vectors before passing them to a transformer network during
self-supervised pre-training (i.e. diagram on the left below).

For fine-tuning, a single linear layer is added on top of the
pre-trained network to train the model on labeled data of audio
downstream tasks such as speech recognition, speech translation and
audio classification (i.e. diagram on the right below).

XLS-R shows impressive improvements over previous state-of-the-art
results on both speech recognition, speech translation and
speaker/language identification, cf. with Table 3-6, Table 7-10, and
Table 11-12 respectively of the official paper.

Setup

In this blog, we will give an in-detail explanation of how XLS-R –
more specifically the pre-trained checkpoint
Wav2Vec2-XLS-R-300M – can be fine-tuned for ASR.

For demonstration purposes, we fine-tune the model on the low resource
ASR dataset of Common
Voice that contains only
ca. 4h of validated training data.

XLS-R is fine-tuned using Connectionist Temporal Classification (CTC),
which is an algorithm that is used to train neural networks for
sequence-to-sequence problems, such as ASR and handwriting recognition.

I highly recommend reading the well-written blog post Sequence
Modeling with CTC (2017) by Awni
Hannun.

Before we start, let’s install datasets and transformers. Also, we
need the torchaudio to load audio files and jiwer to evaluate our
fine-tuned model using the word error rate
(WER) metric ${}^1\{\}^1$

!pip install datasets==1.18.3
!pip install transformers==4.11.3
!pip install huggingface_hub==0.1
!pip install torchaudio
!pip install librosa
!pip install jiwer

We strongly suggest to upload your training checkpoints directly to the
Hugging Face Hub while training. The Hugging Face
Hub has integrated version control so you can
be sure that no model checkpoint is getting lost during training.

To do so you have to store your authentication token from the Hugging
Face website (sign up here if you
haven’t already!)

from huggingface_hub import notebook_login

notebook_login()

Print Output:

    Login successful
    Your token has been saved to /root/.huggingface/token

Then you need to install Git-LFS to upload your model checkpoints:

apt install git-lfs

---

${}^1\{\}^1$

Prepare Data, Tokenizer, Feature Extractor

ASR models transcribe speech to text, which means that we both need a
feature extractor that processes the speech signal to the model’s input
format, e.g. a feature vector, and a tokenizer that processes the
model’s output format to text.

In 🤗 Transformers, the XLS-R model is thus accompanied by both a
tokenizer, called
Wav2Vec2CTCTokenizer,
and a feature extractor, called
Wav2Vec2FeatureExtractor.

Let’s start by creating the tokenizer to decode the predicted output
classes to the output transcription.

Create Wav2Vec2CTCTokenizer

A pre-trained XLS-R model maps the speech signal to a sequence of
context representations as illustrated in the figure above. However, for
speech recognition the model has to to map this sequence of context
representations to its corresponding transcription which means that a
linear layer has to be added on top of the transformer block (shown in
yellow in the diagram above). This linear layer is used to classify
each context representation to a token class analogous to how
a linear layer is added on top of BERT’s embeddings
for further classification after pre-training (cf. with ‘BERT’ section of the following blog
post).
after pretraining a linear layer is added on top of BERT’s embeddings
for further classification – cf. with ‘BERT’ section of this blog
post.

The output size of this layer corresponds to the number of tokens in the
vocabulary, which does not depend on XLS-R’s pretraining task, but
only on the labeled dataset used for fine-tuning. So in the first step,
we will take a look at the chosen dataset of Common Voice and define a
vocabulary based on the transcriptions.

First, let’s go to Common Voice official
website and pick a
language to fine-tune XLS-R on. For this notebook, we will use Turkish.

For each language-specific dataset, you can find a language code
corresponding to your chosen language. On Common
Voice, look for the field
“Version”. The language code then corresponds to the prefix before the
underscore. For Turkish, e.g. the language code is "tr".

Great, now we can use 🤗 Datasets’ simple API to download the data. The
dataset name is "common_voice", the configuration name corresponds to
the language code, which is "tr" in our case.

Common Voice has many different splits including invalidated, which
refers to data that was not rated as “clean enough” to be considered
useful. In this notebook, we will only make use of the splits "train",
"validation" and "test".

Because the Turkish dataset is so small, we will merge both the
validation and training data into a training dataset and only use the
test data for validation.

from datasets import load_dataset, load_metric, Audio

common_voice_train = load_dataset("common_voice", "tr", split="train+validation")
common_voice_test = load_dataset("common_voice", "tr", split="test")

Many ASR datasets only provide the target text, 'sentence' for each
audio array 'audio' and file 'path'. Common Voice actually provides
much more information about each audio file, such as the 'accent',
etc. Keeping the notebook as general as possible, we only consider the
transcribed text for fine-tuning.

common_voice_train = common_voice_train.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"])
common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"])

Let’s write a short function to display some random samples of the
dataset and run it a couple of times to get a feeling for the
transcriptions.

from datasets import ClassLabel
import random
import pandas as pd
from IPython.display import display, HTML

def show_random_elements(dataset, num_examples=10):
    assert num_examples <= len(dataset), "Can't pick more elements than there are in the dataset."
    picks = []
    for _ in range(num_examples):
        pick = random.randint(0, len(dataset)-1)
        while pick in picks:
            pick = random.randint(0, len(dataset)-1)
        picks.append(pick)
    
    df = pd.DataFrame(dataset[picks])
    display(HTML(df.to_html()))

Print Output:

Alright! The transcriptions look fairly clean. Having translated the
transcribed sentences, it seems that the language corresponds more to
written-out text than noisy dialogue. This makes sense considering that
Common Voice is a
crowd-sourced read speech corpus.

We can see that the transcriptions contain some special characters, such
as ,.?!;:. Without a language model, it is much harder to classify
speech chunks to such special characters because they don’t really
correspond to a characteristic sound unit. E.g., the letter "s" has
a more or less clear sound, whereas the special character "." does
not. Also in order to understand the meaning of a speech signal, it is
usually not necessary to include special characters in the
transcription.

Let’s simply remove all characters that don’t contribute to the
meaning of a word and cannot really be represented by an acoustic sound
and normalize the text.

import re
chars_to_remove_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\']'

def remove_special_characters(batch):
    batch["sentence"] = re.sub(chars_to_remove_regex, '', batch["sentence"]).lower()
    return batch

common_voice_train = common_voice_train.map(remove_special_characters)
common_voice_test = common_voice_test.map(remove_special_characters)

Let’s look at the processed text labels again.

show_random_elements(common_voice_train.remove_columns(["path","audio"]))

Print Output:

Good! This looks better. We have removed most special characters from
transcriptions and normalized them to lower-case only.

Before finalizing the pre-processing, it is always advantageous to
consult a native speaker of the target language to see whether the text
can be further simplified. For this blog post,
Merve was kind enough to take a quick
look and noted that “hatted” characters – like â – aren’t really
used anymore in Turkish and can be replaced by their “un-hatted”
equivalent, e.g. a.

This means that we should replace a sentence like
"yargı sistemi hâlâ sağlıksız" to "yargı sistemi hala sağlıksız".

Let’s write another short mapping function to further simplify the text
labels. Remember, the simpler the text labels, the easier it is for the
model to learn to predict those labels.

def replace_hatted_characters(batch):
    batch["sentence"] = re.sub('[â]', 'a', batch["sentence"])
    batch["sentence"] = re.sub('[î]', 'i', batch["sentence"])
    batch["sentence"] = re.sub('[ô]', 'o', batch["sentence"])
    batch["sentence"] = re.sub('[û]', 'u', batch["sentence"])
    return batch

common_voice_train = common_voice_train.map(replace_hatted_characters)
common_voice_test = common_voice_test.map(replace_hatted_characters)

In CTC, it is common to classify speech chunks into letters, so we will
do the same here. Let’s extract all distinct letters of the training
and test data and build our vocabulary from this set of letters.

We write a mapping function that concatenates all transcriptions into
one long transcription and then transforms the string into a set of
chars. It is important to pass the argument batched=True to the
map(...) function so that the mapping function has access to all
transcriptions at once.

def extract_all_chars(batch):
  all_text = " ".join(batch["sentence"])
  vocab = list(set(all_text))
  return {"vocab": [vocab], "all_text": [all_text]}

vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)
vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)

Now, we create the union of all distinct letters in the training dataset
and test dataset and convert the resulting list into an enumerated
dictionary.

vocab_list = list(set(vocab_train["vocab"][0]) | set(vocab_test["vocab"][0]))

vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}
vocab_dict

Print Output:

{
 ' ': 0,
 'a': 1,
 'b': 2,
 'c': 3,
 'd': 4,
 'e': 5,
 'f': 6,
 'g': 7,
 'h': 8,
 'i': 9,
 'j': 10,
 'k': 11,
 'l': 12,
 'm': 13,
 'n': 14,
 'o': 15,
 'p': 16,
 'q': 17,
 'r': 18,
 's': 19,
 't': 20,
 'u': 21,
 'v': 22,
 'w': 23,
 'x': 24,
 'y': 25,
 'z': 26,
 'ç': 27,
 'ë': 28,
 'ö': 29,
 'ü': 30,
 'ğ': 31,
 'ı': 32,
 'ş': 33,
 '̇': 34
}

Cool, we see that all letters of the alphabet occur in the dataset
(which is not really surprising) and we also extracted the special
characters "" and '. Note that we did not exclude those special
characters because:

The model has to learn to predict when a word is finished or else the
model prediction would always be a sequence of chars which would make it
impossible to separate words from each other.

One should always keep in mind that pre-processing is a very important
step before training your model. E.g., we don’t want our model to
differentiate between a and A just because we forgot to normalize
the data. The difference between a and A does not depend on the
“sound” of the letter at all, but more on grammatical rules – e.g.
use a capitalized letter at the beginning of the sentence. So it is
sensible to remove the difference between capitalized and
non-capitalized letters so that the model has an easier time learning to
transcribe speech.

To make it clearer that " " has its own token class, we give it a more
visible character |. In addition, we also add an “unknown” token so
that the model can later deal with characters not encountered in Common
Voice’s training set.

vocab_dict["|"] = vocab_dict[" "]
del vocab_dict[" "]

Finally, we also add a padding token that corresponds to CTC’s “blank
token“. The “blank token” is a core component of the CTC algorithm.
For more information, please take a look at the “Alignment” section
here.

vocab_dict["[UNK]"] = len(vocab_dict)
vocab_dict["[PAD]"] = len(vocab_dict)
len(vocab_dict)

Cool, now our vocabulary is complete and consists of 39 tokens, which
means that the linear layer that we will add on top of the pretrained
XLS-R checkpoint will have an output dimension of 39.

Let’s now save the vocabulary as a json file.

import json
with open('vocab.json', 'w') as vocab_file:
    json.dump(vocab_dict, vocab_file)

In a final step, we use the json file to load the vocabulary into an
instance of the Wav2Vec2CTCTokenizer class.

from transformers import Wav2Vec2CTCTokenizer

tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("./", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")

If one wants to re-use the just created tokenizer with the fine-tuned
model of this notebook, it is strongly advised to upload the tokenizer
to the Hugging Face Hub. Let’s call the repo to which
we will upload the files "wav2vec2-large-xlsr-turkish-demo-colab":

repo_name = "wav2vec2-large-xls-r-300m-tr-colab"

and upload the tokenizer to the 🤗 Hub.

tokenizer.push_to_hub(repo_name)

Great, you can see the just created repository under
https://huggingface.co//wav2vec2-large-xls-r-300m-tr-colab

Create Wav2Vec2FeatureExtractor

Speech is a continuous signal, and, to be treated by computers, it first
has to be discretized, which is usually called sampling. The
sampling rate hereby plays an important role since it defines how many
data points of the speech signal are measured per second. Therefore,
sampling with a higher sampling rate results in a better approximation
of the real speech signal but also necessitates more values per
second.

A pretrained checkpoint expects its input data to have been sampled more
or less from the same distribution as the data it was trained on. The
same speech signals sampled at two different rates have a very different
distribution. For example, doubling the sampling rate results in data points
being twice as long. Thus, before fine-tuning a pretrained checkpoint of
an ASR model, it is crucial to verify that the sampling rate of the data
that was used to pretrain the model matches the sampling rate of the
dataset used to fine-tune the model.

XLS-R was pretrained on audio data of
Babel,
Multilingual LibriSpeech
(MLS),
Common Voice,
VoxPopuli, and
VoxLingua107 at a sampling rate of
16kHz. Common Voice, in its original form, has a sampling rate of 48kHz,
thus we will have to downsample the fine-tuning data to 16kHz in the
following.

A Wav2Vec2FeatureExtractor object requires the following parameters to
be instantiated:

• feature_size: Speech models take a sequence of feature vectors as
  an input. While the length of this sequence obviously varies, the
  feature size should not. In the case of Wav2Vec2, the feature size
  is 1 because the model was trained on the raw speech signal ${}^2\{\}^2$
• sampling_rate: The sampling rate at which the model is trained on.
• padding_value: For batched inference, shorter inputs need to be
  padded with a specific value
• do_normalize: Whether the input should be
  zero-mean-unit-variance normalized or not. Usually, speech models
  perform better when normalizing the input
• return_attention_mask: Whether the model should make use of an
  attention_mask for batched inference. In general, XLS-R models
  checkpoints should always use the attention_mask.

from transformers import Wav2Vec2FeatureExtractor

feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)

Great, XLS-R’s feature extraction pipeline is thereby fully defined!

For improved user-friendliness, the feature extractor and tokenizer are
wrapped into a single Wav2Vec2Processor class so that one only needs
a model and processor object.

from transformers import Wav2Vec2Processor

processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)

Next, we can prepare the dataset.

Preprocess Data

So far, we have not looked at the actual values of the speech signal but
just the transcription. In addition to sentence, our datasets include
two more column names path and audio. path states the absolute
path of the audio file. Let’s take a look.

common_voice_train[0]["path"]

XLS-R expects the input in the format of a 1-dimensional array of 16
kHz. This means that the audio file has to be loaded and resampled.

Thankfully, datasets does this automatically by calling the other
column audio. Let try it out.

common_voice_train[0]["audio"]

    {'array': array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
            -8.8930130e-05, -3.8027763e-05, -2.9146671e-05], dtype=float32),
     'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3',
     'sampling_rate': 48000}

Great, we can see that the audio file has automatically been loaded.
This is thanks to the new "Audio"
feature
introduced in datasets == 1.18.3, which loads and resamples audio
files on-the-fly upon calling.

In the example above we can see that the audio data is loaded with a
sampling rate of 48kHz whereas 16kHz are expected by the model. We can
set the audio feature to the correct sampling rate by making use of
cast_column:

common_voice_train = common_voice_train.cast_column("audio", Audio(sampling_rate=16_000))
common_voice_test = common_voice_test.cast_column("audio", Audio(sampling_rate=16_000))

Let’s take a look at "audio" again.

common_voice_train[0]["audio"]

    {'array': array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
            -7.4556941e-05, -1.4621433e-05, -5.7861507e-05], dtype=float32),
     'path': '/root/.cache/huggingface/datasets/downloads/extracted/05be0c29807a73c9b099873d2f5975dae6d05e9f7d577458a2466ecb9a2b0c6b/cv-corpus-6.1-2020-12-11/tr/clips/common_voice_tr_21921195.mp3',
     'sampling_rate': 16000}

This seemed to have worked! Let’s listen to a couple of audio files to
better understand the dataset and verify that the audio was correctly
loaded.

import IPython.display as ipd
import numpy as np
import random

rand_int = random.randint(0, len(common_voice_train)-1)

print(common_voice_train[rand_int]["sentence"])
ipd.Audio(data=common_voice_train[rand_int]["audio"]["array"], autoplay=True, rate=16000)

Print Output:

    sunulan bütün teklifler i̇ngilizce idi

It seems like the data is now correctly loaded and resampled.

It can be heard, that the speakers change along with their speaking
rate, accent, and background environment, etc. Overall, the recordings
sound acceptably clear though, which is to be expected from a
crowd-sourced read speech corpus.

Let’s do a final check that the data is correctly prepared, by printing
the shape of the speech input, its transcription, and the corresponding
sampling rate.

rand_int = random.randint(0, len(common_voice_train)-1)

print("Target text:", common_voice_train[rand_int]["sentence"])
print("Input array shape:", common_voice_train[rand_int]["audio"]["array"].shape)
print("Sampling rate:", common_voice_train[rand_int]["audio"]["sampling_rate"])

Print Output:

    Target text: makedonya bu yıl otuz adet tyetmiş iki tankı aldı
    Input array shape: (71040,)
    Sampling rate: 16000

Good! Everything looks fine – the data is a 1-dimensional array, the
sampling rate always corresponds to 16kHz, and the target text is
normalized.

Finally, we can leverage Wav2Vec2Processor to process the data to the
format expected by Wav2Vec2ForCTC for training. To do so let’s make
use of Dataset’s
map(...)
function.

First, we load and resample the audio data, simply by calling
batch["audio"]. Second, we extract the input_values from the loaded
audio file. In our case, the Wav2Vec2Processor only normalizes the
data. For other speech models, however, this step can include more
complex feature extraction, such as Log-Mel feature
extraction.
Third, we encode the transcriptions to label ids.

Note: This mapping function is a good example of how the
Wav2Vec2Processor class should be used. In “normal” context, calling
processor(...) is redirected to Wav2Vec2FeatureExtractor‘s call
method. When wrapping the processor into the as_target_processor
context, however, the same method is redirected to
Wav2Vec2CTCTokenizer‘s call method. For more information please check
the
docs.

def prepare_dataset(batch):
    audio = batch["audio"]

    
    batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
    batch["input_length"] = len(batch["input_values"])
    
    with processor.as_target_processor():
        batch["labels"] = processor(batch["sentence"]).input_ids
    return batch

Let’s apply the data preparation function to all examples.

common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)
common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)

Note: Currently datasets make use of
torchaudio and
librosa for audio loading
and resampling. If you wish to implement your own costumized data
loading/sampling, feel free to just make use of the "path" column
instead and disregard the "audio" column.

Long input sequences require a lot of memory. XLS-R is based on
self-attention. The memory requirement scales quadratically with the
input length for long input sequences (cf. with
this
reddit post). In case this demo crashes with an “Out-of-memory” error
for you, you might want to uncomment the following lines to filter all
sequences that are longer than 5 seconds for training.

Awesome, now we are ready to start training!

Training

The data is processed so that we are ready to start setting up the
training pipeline. We will make use of 🤗’s
Trainer
for which we essentially need to do the following:

• Define a data collator. In contrast to most NLP models, XLS-R has a
  much larger input length than output length. E.g., a sample of
  input length 50000 has an output length of no more than 100. Given
  the large input sizes, it is much more efficient to pad the training
  batches dynamically meaning that all training samples should only be
  padded to the longest sample in their batch and not the overall
  longest sample. Therefore, fine-tuning XLS-R requires a special
  padding data collator, which we will define below

• Evaluation metric. During training, the model should be evaluated on
  the word error rate. We should define a compute_metrics function
  accordingly

• Load a pretrained checkpoint. We need to load a pretrained
  checkpoint and configure it correctly for training.

• Define the training configuration.

After having fine-tuned the model, we will correctly evaluate it on the
test data and verify that it has indeed learned to correctly transcribe
speech.

Set-up Trainer

Let’s start by defining the data collator. The code for the data
collator was copied from this
example.

Without going into too many details, in contrast to the common data
collators, this data collator treats the input_values and labels
differently and thus applies to separate padding functions on them
(again making use of XLS-R processor’s context manager). This is
necessary because in speech input and output are of different modalities
meaning that they should not be treated by the same padding function.
Analogous to the common data collators, the padding tokens in the labels
with -100 so that those tokens are not taken into account when
computing the loss.

import torch

from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

@dataclass
class DataCollatorCTCWithPadding:
    """
    Data collator that will dynamically pad the inputs received.
    Args:
        processor (:class:`~transformers.Wav2Vec2Processor`)
            The processor used for processing the data.
        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:
            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
              sequence if provided).
            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
              maximum acceptable input length for the model if that argument is not provided.
            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
              different lengths).
    """

    processor: Wav2Vec2Processor
    padding: Union[bool, str] = True

    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        
        
        input_features = [{"input_values": feature["input_values"]} for feature in features]
        label_features = [{"input_ids": feature["labels"]} for feature in features]

        batch = self.processor.pad(
            input_features,
            padding=self.padding,
            return_tensors="pt",
        )
        with self.processor.as_target_processor():
            labels_batch = self.processor.pad(
                label_features,
                padding=self.padding,
                return_tensors="pt",
            )

        
        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)

        batch["labels"] = labels

        return batch

data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)

Next, the evaluation metric is defined. As mentioned earlier, the
predominant metric in ASR is the word error rate (WER), hence we will
use it in this notebook as well.

wer_metric = load_metric("wer")

The model will return a sequence of logit vectors: ${\mathbf{y}}_1,\dots ,{\mathbf{y}}_m\backslash mathbf\{y\}\_1,\; \backslash ldots,\; \backslash mathbf\{y\}\_m$

A logit vector ${\mathbf{y}}_1\backslash mathbf\{y\}\_1$

def compute_metrics(pred):
    pred_logits = pred.predictions
    pred_ids = np.argmax(pred_logits, axis=-1)

    pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id

    pred_str = processor.batch_decode(pred_ids)
    
    label_str = processor.batch_decode(pred.label_ids, group_tokens=False)

    wer = wer_metric.compute(predictions=pred_str, references=label_str)

    return {"wer": wer}

Now, we can load the pretrained checkpoint of
Wav2Vec2-XLS-R-300M.
The tokenizer’s pad_token_id must be to define the model’s
pad_token_id or in the case of Wav2Vec2ForCTC also CTC’s blank
token ${}^2\{\}^2$

Because the dataset is quite small (~6h of training data) and because
Common Voice is quite noisy, fine-tuning Facebook’s
wav2vec2-xls-r-300m checkpoint seems to require some
hyper-parameter tuning. Therefore, I had to play around a bit with
different values for dropout,
SpecAugment‘s masking dropout rate,
layer dropout, and the learning rate until training seemed to be stable
enough.

Note: When using this notebook to train XLS-R on another language of
Common Voice those hyper-parameter settings might not work very well.
Feel free to adapt those depending on your use case.

from transformers import Wav2Vec2ForCTC

model = Wav2Vec2ForCTC.from_pretrained(
    "facebook/wav2vec2-xls-r-300m", 
    attention_dropout=0.0,
    hidden_dropout=0.0,
    feat_proj_dropout=0.0,
    mask_time_prob=0.05,
    layerdrop=0.0,
    ctc_loss_reduction="mean", 
    pad_token_id=processor.tokenizer.pad_token_id,
    vocab_size=len(processor.tokenizer),
)

The first component of XLS-R consists of a stack of CNN layers that are
used to extract acoustically meaningful – but contextually independent –
features from the raw speech signal. This part of the model has already
been sufficiently trained during pretraining and as stated in the
paper does not need to be
fine-tuned anymore. Thus, we can set the requires_grad to False for
all parameters of the feature extraction part.

model.freeze_feature_extractor()

In a final step, we define all parameters related to training. To give
more explanation on some of the parameters:

• group_by_length makes training more efficient by grouping training
  samples of similar input length into one batch. This can
  significantly speed up training time by heavily reducing the overall
  number of useless padding tokens that are passed through the model
• learning_rate and weight_decay were heuristically tuned until
  fine-tuning has become stable. Note that those parameters strongly
  depend on the Common Voice dataset and might be suboptimal for other
  speech datasets.

For more explanations on other parameters, one can take a look at the
docs.

During training, a checkpoint will be uploaded asynchronously to the Hub
every 400 training steps. It allows you to also play around with the
demo widget even while your model is still training.

Note: If one does not want to upload the model checkpoints to the
Hub, simply set push_to_hub=False.

from transformers import TrainingArguments

training_args = TrainingArguments(
  output_dir=repo_name,
  group_by_length=True,
  per_device_train_batch_size=16,
  gradient_accumulation_steps=2,
  evaluation_strategy="steps",
  num_train_epochs=30,
  gradient_checkpointing=True,
  fp16=True,
  save_steps=400,
  eval_steps=400,
  logging_steps=400,
  learning_rate=3e-4,
  warmup_steps=500,
  save_total_limit=2,
  push_to_hub=True,
)

Now, all instances can be passed to Trainer and we are ready to start
training!

from transformers import Trainer

trainer = Trainer(
    model=model,
    data_collator=data_collator,
    args=training_args,
    compute_metrics=compute_metrics,
    train_dataset=common_voice_train,
    eval_dataset=common_voice_test,
    tokenizer=processor.feature_extractor,
)

---

${}^1\{\}^1$

Training

Training will take multiple hours depending on the GPU allocated to this
notebook. While the trained model yields somewhat satisfying results on
Common Voice‘s test data of Turkish, it is by no means an optimally
fine-tuned model. The purpose of this notebook is just to demonstrate
how to fine-tune XLS-R XLSR-Wav2Vec2’s on an ASR dataset.

Depending on what GPU was allocated to your google colab it might be
possible that you are seeing an "out-of-memory" error here. In this
case, it’s probably best to reduce per_device_train_batch_size to 8
or even less and increase
gradient_accumulation.

trainer.train()

Print Output:

The training loss and validation WER go down nicely.

You can now upload the result of the training to the Hub, just execute
this instruction:

trainer.push_to_hub()

You can now share this model with all your friends, family, favorite
pets: they can all load it with the identifier
“your-username/the-name-you-picked” so for instance:

from transformers import AutoModelForCTC, Wav2Vec2Processor

model = AutoModelForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab")
processor = Wav2Vec2Processor.from_pretrained("patrickvonplaten/wav2vec2-large-xls-r-300m-tr-colab")

For more examples of how XLS-R can be fine-tuned, please take a look at the official
🤗 Transformers examples.

Evaluation

As a final check, let’s load the model and verify that it indeed has
learned to transcribe Turkish speech.

Let’s first load the pretrained checkpoint.

model = Wav2Vec2ForCTC.from_pretrained(repo_name).to("cuda")
processor = Wav2Vec2Processor.from_pretrained(repo_name)

Now, we will just take the first example of the test set, run it through
the model and take the argmax(...) of the logits to retrieve the
predicted token ids.

input_dict = processor(common_voice_test[0]["input_values"], return_tensors="pt", padding=True)

logits = model(input_dict.input_values.to("cuda")).logits

pred_ids = torch.argmax(logits, dim=-1)[0]

It is strongly recommended to pass the sampling_rate argument to this function.Failing to do so can result in silent errors that might be hard to debug.

We adapted common_voice_test quite a bit so that the dataset instance
does not contain the original sentence label anymore. Thus, we re-use
the original dataset to get the label of the first example.

common_voice_test_transcription = load_dataset("common_voice", "tr", data_dir="./cv-corpus-6.1-2020-12-11", split="test")

Finally, we can decode the example.

print("Prediction:")
print(processor.decode(pred_ids))

print("\nReference:")
print(common_voice_test_transcription[0]["sentence"].lower())

Print Output:

Alright! The transcription can definitely be recognized from our
prediction, but it is not perfect yet. Training the model a bit longer,
spending more time on the data preprocessing, and especially using a
language model for decoding would certainly improve the model’s overall
performance.

For a demonstration model on a low-resource language, the results are
quite acceptable however 🤗.