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Fine-tuned facebook/wav2vec2-large-xlsr-53 on Kinyarwanda using the Common Voice dataset, using about 25% of the training data (limited to utterances without downvotes and shorter with 9.5 seconds), and validated on 2048 utterances from the validation set. In contrast to the lucio/wav2vec2-large-xlsr-kinyarwanda model, which does not predict any punctuation, this model attempts to predict the apostrophes that mark contractions of pronouns with vowel-initial words, but may overgeneralize. When using this model, make sure that your speech input is sampled at 16kHz.


The model can be used directly (without a language model) as follows:

import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

# WARNING! This will download and extract to use about 80GB on disk.
test_dataset = load_dataset("common_voice", "rw", split="test[:2%]") 

processor = Wav2Vec2Processor.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda") 
model = Wav2Vec2ForCTC.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])


Prediction: ['yaherukago gukora igitaramo yiki mujyiwa na mor mu bubiligi', "ibi rero ntibizashoboka kandi n'umudabizi"]
Reference: ['Yaherukaga gukora igitaramo nk’iki mu Mujyi wa Namur mu Bubiligi.', 'Ibi rero, ntibizashoboka, kandi nawe arabizi.']


The model can be evaluated as follows on the Kinyarwanda test data of Common Voice. Note that to even load the test data, the whole 40GB Kinyarwanda dataset will be downloaded and extracted into another 40GB directory, so you will need that space available on disk (e.g. not possible in the free tier of Google Colab). This script uses the chunked_wer function from pcuenq.

import jiwer
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import unidecode

test_dataset = load_dataset("common_voice", "rw", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda-apostrophied") 
model = Wav2Vec2ForCTC.from_pretrained("lucio/wav2vec2-large-xlsr-kinyarwanda-apostrophied")

chars_to_ignore_regex = r'[!"#$%&()*+,./:;<=>?@\[\]\\_{}|~£¤¨©ª«¬®¯°·¸»¼½¾ðʺ˜˝ˮ‐–—―‚“”„‟•…″‽₋€™−√�]'

def remove_special_characters(batch):
    batch["text"] = re.sub(r'[ʻʽʼ‘’´`]', r"'", batch["sentence"])  # normalize apostrophes
    batch["text"] = re.sub(chars_to_ignore_regex, "", batch["text"]).lower().strip()  # remove all other punctuation
    batch["text"] = re.sub(r"([b-df-hj-np-tv-z])' ([aeiou])", r"\1'\2", batch["text"])  # remove spaces where apostrophe marks a deleted vowel
    batch["text"] = re.sub(r"(-| '|' |  +)", " ", batch["text"])  # treat dash and other apostrophes as word boundary
    batch["text"] = unidecode.unidecode(batch["text"])  # strip accents from loanwords
    return batch

## Audio pre-processing
resampler = torchaudio.transforms.Resample(48_000, 16_000)

def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    batch["sampling_rate"] = 16_000
    return batch

def cv_prepare(batch):
    batch = remove_special_characters(batch)
    batch = speech_file_to_array_fn(batch)
    return batch
test_dataset = test_dataset.map(cv_prepare)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

def chunked_wer(targets, predictions, chunk_size=None):                                          
    if chunk_size is None: return jiwer.wer(targets, predictions)                                
    start = 0                                                                                    
    end = chunk_size                                                                             
    H, S, D, I = 0, 0, 0, 0                                                                      
    while start < len(targets):                                                                  
        chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])       
        H = H + chunk_metrics["hits"]                                                            
        S = S + chunk_metrics["substitutions"]                                                   
        D = D + chunk_metrics["deletions"]                                                       
        I = I + chunk_metrics["insertions"]                                                      
        start += chunk_size                                                                      
        end += chunk_size                                                                        
    return float(S + D + I) / float(H + S + D)

print("WER: {:2f}".format(100 * chunked_wer(result["sentence"], result["pred_strings"], chunk_size=4000)))

Test Result: 39.92 %


Examples from the Common Voice training dataset were used for training, after filtering out utterances that had any down_vote or were longer than 9.5 seconds. The data used totals about 125k examples, 25% of the available data, trained on 1 V100 GPU provided by OVHcloud, for a total of about 60 hours: 20 epochs on one block of 32k examples and then 10 epochs each on 3 more blocks of 32k examples. For validation, 2048 examples of the validation dataset were used.

The script used for training is adapted from the example script provided in the transformers repo.

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Dataset used to train lucio/wav2vec2-large-xlsr-kinyarwanda-apostrophied

Evaluation results