valhalla/s2t_librispeech_large

TODO: [To be filled]

Evaluation on LibriSpeech Test

The following script shows how to evaluate this model on the LibriSpeech "clean" and "other" test dataset.

from datasets import load_dataset
from transformers import Speech2TextTransformerForConditionalGeneration, Speech2TextTransformerTokenizer
import soundfile as sf
from jiwer import wer

librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")  # change to "other" for other test dataset

model = Speech2TextTransformerForConditionalGeneration.from_pretrained("valhalla/s2t_librispeech_large").to("cuda")
tokenizer = Speech2TextTransformerTokenizer.from_pretrained("valhalla/s2t_librispeech_large", do_upper_case=True)

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

librispeech_eval = librispeech_eval.map(map_to_array)

def map_to_pred(batch):
    features = tokenizer(batch["speech"], sample_rate=16000, padding=True, return_tensors="pt")
    input_features = features.input_features.to("cuda")
    attention_mask = features.attention_mask.to("cuda")

    gen_tokens = model.generate(input_ids=input_features, attention_mask=attention_mask)
    batch["transcription"] = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)
    return batch

result = librispeech_eval.map(map_to_pred, batched=True, batch_size=8, remove_columns=["speech"])

print("WER:", wer(result["text"], result["transcription"]))

Result (WER):

"clean"	"other"
3.3	7.5