Fine-tuning XLS-R for Multi-Lingual 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:

• Wav2Vec2-XLS-R-300M
• Wav2Vec2-XLS-R-1B
• Wav2Vec2-XLS-R-2B

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 ${}^1$.

!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

---

${}^1$ In the paper, the model was evaluated using the phoneme error rate (PER), but by far the most common metric in ASR is the word error rate (WER). To keep this notebook as general as possible we decided to evaluate the model using WER.

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./<your-username>/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 ${}^2$.
• 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"]

    # batched output is "un-batched"
    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.

#max_input_length_in_sec = 5.0
#common_voice_train = common_voice_train.filter(lambda x: x < max_input_length_in_sec * processor.feature_extractor.sampling_rate, input_columns=["input_length"])

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 proccessing 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]:
        # split inputs and labels since they have to be of different lengths and need
        # different padding methods
        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",
            )

        # replace padding with -100 to ignore loss correctly
        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: $\mathbf{y}_1, \ldots, \mathbf{y}_m$ with $\mathbf{y}_1 = f_{\theta}(x_1, \ldots, x_n)[0]$ and $n >> m$.

A logit vector $\mathbf{y}_1$ contains the log-odds for each word in the vocabulary we defined earlier, thus $\text{len}(\mathbf{y}_i) =$ config.vocab_size. We are interested in the most likely prediction of the model and thus take the argmax(...) of the logits. Also, we transform the encoded labels back to the original string by replacing -100 with the pad_token_id and decoding the ids while making sure that consecutive tokens are not grouped to the same token in CTC style ${}^1$.

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)
    # we do not want to group tokens when computing the metrics
    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 ${}^2$. To save GPU memory, we enable PyTorch's gradient checkpointing and also set the loss reduction to "mean".

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,
)

---

${}^1$ To allow models to become independent of the speaker rate, in CTC, consecutive tokens that are identical are simply grouped as a single token. However, the encoded labels should not be grouped when decoding since they don't correspond to the predicted tokens of the model, which is why the group_tokens=False parameter has to be passed. If we wouldn't pass this parameter a word like "hello" would incorrectly be encoded, and decoded as "helo". ${}^2$ The blank token allows the model to predict a word, such as "hello" by forcing it to insert the blank token between the two l's. A CTC-conform prediction of "hello" of our model would be [PAD] [PAD] "h" "e" "e" "l" "l" [PAD] "l" "o" "o" [PAD].

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 🤗.