wav2vec2-large-xlsr-53-english-ZipNN-Compressed

Disclaimer and Requirements

This model is a clone of jonatasgrosman/wav2vec2-large-xlsr-53-english compressed using ZipNN. Compressed losslessly to 88% its original size, ZipNN saved ~0.2GB in storage and potentially ~4PB in data transfer monthly.

Requirement

In order to use the model, ZipNN is necessary:

pip install zipnn

Use This Model

# Use a pipeline as a high-level helper
from transformers import pipeline
from zipnn import zipnn_hf

zipnn_hf()


pipe = pipeline("automatic-speech-recognition", model="royleibov/wav2vec2-large-xlsr-53-english-ZipNN-Compressed")

# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
from zipnn import zipnn_hf

zipnn_hf()

processor = AutoProcessor.from_pretrained("royleibov/wav2vec2-large-xlsr-53-english-ZipNN-Compressed")
model = AutoModelForCTC.from_pretrained("royleibov/wav2vec2-large-xlsr-53-english-ZipNN-Compressed")

ZipNN

ZipNN also allows you to seemlessly save local disk space in your cache after the model is downloaded.

To compress the cached model, simply run:

python zipnn_compress_path.py safetensors --model royleibov/wav2vec2-large-xlsr-53-english-ZipNN-Compressed --hf_cache

The model will be decompressed automatically and safely as long as zipnn_hf() is added at the top of the file like in the example above.

To decompress manualy, simply run:

python zipnn_decompress_path.py --model royleibov/wav2vec2-large-xlsr-53-english-ZipNN-Compressed --hf_cache

Fine-tuned XLSR-53 large model for speech recognition in English

Fine-tuned facebook/wav2vec2-large-xlsr-53 on English using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz.

This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :)

The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint

Usage

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

Using the HuggingSound library:

from huggingsound import SpeechRecognitionModel
from zipnn import zipnn_hf

zipnn_hf()

model = SpeechRecognitionModel("royleibov/wav2vec2-large-xlsr-53-english-ZipNN-Compressed")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]

transcriptions = model.transcribe(audio_paths)

Writing your own inference script:

import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from zipnn import zipnn_hf

zipnn_hf()

LANG_ID = "en"
MODEL_ID = "royleibov/wav2vec2-large-xlsr-53-english-ZipNN-Compressed"
SAMPLES = 10

test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
    batch["speech"] = speech_array
    batch["sentence"] = batch["sentence"].upper()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["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)
predicted_sentences = processor.batch_decode(predicted_ids)

for i, predicted_sentence in enumerate(predicted_sentences):
    print("-" * 100)
    print("Reference:", test_dataset[i]["sentence"])
    print("Prediction:", predicted_sentence)

Evaluation

1. To evaluate on mozilla-foundation/common_voice_6_0 with split test

python eval.py --model_id royleibov/wav2vec2-large-xlsr-53-english-ZipNN-Compressed --dataset mozilla-foundation/common_voice_6_0 --config en --split test

1. To evaluate on speech-recognition-community-v2/dev_data

python eval.py --model_id royleibov/wav2vec2-large-xlsr-53-english-ZipNN-Compressed --dataset speech-recognition-community-v2/dev_data --config en --split validation --chunk_length_s 5.0 --stride_length_s 1.0

Citation

If you want to cite this model you can use this:

@misc{grosman2021xlsr53-large-english,
  title={Fine-tuned {XLSR}-53 large model for speech recognition in {E}nglish},
  author={Grosman, Jonatas},
  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english}},
  year={2021}
}