Wav2vec2 Large Xls R 300m German With Lm

🚀 wav2vec2-large-xls-r-300m-german-with-lm

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the German set of the Common Voice dataset. It achieves a Word Error Rate of 8.8% on the evaluation set, providing high - quality speech recognition for German.

✨ Features

• Fine - tuned on the German set of the Common Voice dataset.
• Achieves a relatively low Word Error Rate of 8.8% on the evaluation set.
• Comes with a full - fledged training and evaluation codebase.

📚 Documentation

Model description

German wav2vec2 - xls - r - 300m trained on the full train set of Common Voice dataset with a n - gram language model. Full code available in my Github repository

Citation

Feel free to cite this work by

@misc{mfleck/wav2vec2-large-xls-r-300m-german-with-lm,
  title={XLS-R-300 Wav2Vec2 German with language model},
  author={Fleck, Michael},
  publisher={Hugging Face},
  journal={Hugging Face Hub},
  howpublished={\url{https://huggingface.co/mfleck/wav2vec2-large-xls-r-300m-german-with-lm}},
  year={2022}
}

Intended uses & limitations

Inference Usage

from transformers import pipeline

pipe = pipeline(model="mfleck/wav2vec2-large-xls-r-300m-german-with-lm")
output = pipe("/path/to/file.wav",chunk_length_s=5, stride_length_s=1)
print(output["text"])

Training and evaluation data

Script used for training (takes about 80 hours on a single A100 40GB)

import random
import re
import json
from typing import Any, Dict, List, Optional, Union

import pandas as pd
import numpy as np
import torch
# import soundfile

from datasets import load_dataset, load_metric, Audio
from dataclasses import dataclass, field

from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, TrainingArguments, Trainer, Wav2Vec2ForCTC


'''
    Most parts of this script are following the tutorial: https://huggingface.co/blog/fine-tune-xlsr-wav2vec2
'''


common_voice_train = load_dataset("common_voice", "de", split="train+validation")
# Use train dataset with less training data
#common_voice_train = load_dataset("common_voice", "de", split="train[:3%]")
common_voice_test = load_dataset("common_voice", "de", split="test")


# Remove unused columns
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"])


# Remove batches with chars which do not exist in German
print(len(common_voice_train))
regex = "[^A-Za-zäöüÄÖÜß,?.! ]+"
common_voice_train = common_voice_train.filter(lambda example: bool(re.search(regex, example['sentence']))==False)
common_voice_test = common_voice_test.filter(lambda example: bool(re.search(regex, example['sentence']))==False)
print(len(common_voice_train))


# Remove special chars from transcripts
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, num_proc=10)
common_voice_test = common_voice_test.map(remove_special_characters, num_proc=10)


# Show some random transcripts to proof that preprocessing worked as expected
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)
    
    print(str(dataset[picks]))
show_random_elements(common_voice_train.remove_columns(["path","audio"]))


# Extract all chars which exist in datasets and add wav2vek tokens
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)

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
vocab_dict["|"] = vocab_dict[" "]
del vocab_dict[" "]
vocab_dict["[UNK]"] = len(vocab_dict)
vocab_dict["[PAD]"] = len(vocab_dict)
len(vocab_dict)
with open('vocab.json', 'w') as vocab_file:
    json.dump(vocab_dict, vocab_file)



# Create tokenizer and repo at Huggingface
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("./", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")
repo_name = "wav2vec2-large-xls-r-300m-german-with-lm"
tokenizer.push_to_hub(repo_name)
print("pushed to hub")



# Create feature extractor and processor
feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)
processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)


# Cast audio 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))


# Convert audio signal to array and 16khz sampling rate
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]
    # Save an audio file to check if it gets loaded correctly
    # soundfile.write("/home/debian/trainnew/test.wav",batch["input_values"],audio["sampling_rate"])
    batch["input_length"] = len(batch["input_values"])
    
    with processor.as_target_processor():
        batch["labels"] = processor(batch["sentence"]).input_ids
    return batch

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)
print("dataset prepared")




@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 lenghts 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)


# Use word error rate as metric
wer_metric = load_metric("wer")
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}



# Model and training parameters
model = Wav2Vec2ForCTC.from_pretrained(
    "facebook/wav2vec2-xls-r-300m", 
    attention_dropout=0.094,
    hidden_dropout=0.01,
    feat_proj_dropout=0.04,
    mask_time_prob=0.08,
    layerdrop=0.04,
    ctc_loss_reduction="mean", 
    pad_token_id=processor.tokenizer.pad_token_id,
    vocab_size=len(processor.tokenizer),
)
model.freeze_feature_extractor()

training_args = TrainingArguments(
  output_dir=repo_name,
  group_by_length=True,
  per_device_train_batch_size=32,
  gradient_accumulation_steps=2,
  evaluation_strategy="steps",
  num_train_epochs=20,
  gradient_checkpointing=True,
  fp16=True,
  save_steps=5000,
  eval_steps=5000,
  logging_steps=100,
  learning_rate=1e-4,
  warmup_steps=500,
  save_total_limit=3,
  push_to_hub=True,
)

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,
)

# Start fine tuning
trainer.train()

# When done push final model to Huggingface hub
trainer.push_to_hub()

The model achieves a Word Error Rate of 8.8% using the following script:

import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline



# load dataset
dataset = load_dataset("common_voice", "de", split="test")
# use only 1% of data
#dataset = load_dataset("common_voice", "de", split="test[:1%]")


# load processor
feature_extractor = AutoFeatureExtractor.from_pretrained("mfleck/wav2vec2-large-xls-r-300m-german-with-lm")
sampling_rate = feature_extractor.sampling_rate

dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))

# load eval pipeline
# device=0 means GPU, use device=-1 for CPU
asr = pipeline("automatic-speech-recognition", model="mfleck/wav2vec2-large-xls-r-300m-german-with-lm", device=0)

# Remove batches with chars which do not exist in German
regex = "[^A-Za-zäöüÄÖÜß,?.! ]+"
dataset = dataset.filter(lambda example: bool(re.search(regex, example['sentence']))==False)

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\']'
# map function to decode audio
def map_to_pred(batch):
    prediction = asr(batch["audio"]["array"], chunk_length_s=5, stride_length_s=1)

    # Print automatic generated transcript
    #print(str(prediction))

    batch["prediction"] = prediction["text"]
    text = batch["sentence"]
    batch["target"] = re.sub(chars_to_ignore_regex, "", text.lower()) + " "
    
    return batch

# run inference on all examples
result = dataset.map(map_to_pred, remove_columns=dataset.column_names)

# load metric
wer = load_metric("wer")
cer = load_metric("cer")

# compute metrics
wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
cer_result = cer.compute(references=result["target"], predictions=result["prediction"])

# print results
result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
print(result_str)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

📄 License

This project is licensed under the Apache - 2.0 license.