S2t Small Covost2 Ca En St

🚀 S2T-SMALL-COVOST2-CA-EN-ST

s2t-small-covost2-ca-en-st is a Speech to Text Transformer (S2T) model designed for end-to-end Speech Translation (ST), enabling seamless conversion from Catalan speech to English text.

🚀 Quick Start

This model can be used for end-to-end Catalan speech to English text translation. As this is a standard sequence to sequence transformer model, you can use the generate method to generate the transcripts by passing the speech features to the model.

Note: The Speech2TextProcessor object uses torchaudio to extract the filter bank features. Make sure to install the torchaudio package before running this example.

You could either install those as extra speech dependancies with pip install transformers"[speech, sentencepiece]" or install the packages separately with pip install torchaudio sentencepiece.

import torch
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
from datasets import load_dataset
import soundfile as sf

model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-ca-en-st")
processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-ca-en-st")

def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch

ds = load_dataset(
    "patrickvonplaten/librispeech_asr_dummy",
    "clean",
    split="validation"
)
ds = ds.map(map_to_array)

inputs = processor(
    ds["speech"][0],
    sampling_rate=48_000,
    return_tensors="pt"
)
generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])

translation = processor.batch_decode(generated_ids, skip_special_tokens=True)

✨ Features

• End - to - End Translation: Capable of directly translating Catalan speech to English text.
• Transformer - Based: Utilizes a transformer-based seq2seq (encoder - decoder) architecture.
• Convolutional Downsampler: Reduces the length of speech inputs by 3/4th before feeding them into the encoder.

📦 Installation

You could either install the necessary packages as extra speech dependancies with pip install transformers"[speech, sentencepiece]" or install the packages separately with pip install torchaudio sentencepiece.

💻 Usage Examples

Basic Usage

import torch
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
from datasets import load_dataset
import soundfile as sf

model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-ca-en-st")
processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-ca-en-st")

def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch

ds = load_dataset(
    "patrickvonplaten/librispeech_asr_dummy",
    "clean",
    split="validation"
)
ds = ds.map(map_to_array)

inputs = processor(
    ds["speech"][0],
    sampling_rate=48_000,
    return_tensors="pt"
)
generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])

translation = processor.batch_decode(generated_ids, skip_special_tokens=True)

📚 Documentation

Model description

S2T is a transformer-based seq2seq (encoder - decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively.

Intended uses & limitations

This model can be used for end-to-end Catalan speech to English text translation. See the model hub to look for other S2T checkpoints.

Training data

The s2t-small-covost2-ca-en-st is trained on Catalan - English subset of CoVoST2. CoVoST is a large-scale multilingual ST corpus based on Common Voice, created to foster ST research with the largest ever open dataset.

Training procedure

Preprocessing

The speech data is pre - processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization) is applied to each example.

The texts are lowercased and tokenized using character based SentencePiece vocab.

Training

The model is trained with standard autoregressive cross-entropy loss and using SpecAugment. The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate model training and for better performance the encoder is pre-trained for English ASR.

Evaluation results

CoVOST2 test results for ca - en (BLEU score): 17.85

🔧 Technical Details

• Model Architecture: Transformer-based seq2seq (encoder - decoder) model.
• Downsampling: Convolutional downsampler reduces speech input length by 3/4th.
• Loss Function: Standard autoregressive cross-entropy loss.
• Pre - training: The encoder is pre-trained for English ASR.

📄 License

This model is licensed under the MIT license.

BibTeX entry and citation info

@inproceedings{wang2020fairseqs2t,
  title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
  author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
  booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
  year = {2020},
}

⚠️ Important Note

The Speech2TextProcessor object uses torchaudio to extract the filter bank features. Make sure to install the torchaudio package before running this example.

💡 Usage Tip

You could either install those as extra speech dependancies with pip install transformers"[speech, sentencepiece]" or install the packages separately with pip install torchaudio sentencepiece.