
TODO: [To be filled]


## Evaluation on LibriSpeech Test

The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr) *"clean"* and *"other"* test dataset.

```python
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_medium").to("cuda")
tokenizer = Speech2TextTransformerTokenizer.from_pretrained("valhalla/s2t_librispeech_medium", 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" |
|---|---|
| 3.5 | 7.8 |
{"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"]}
transformers pytorch speech_to_text_transformer text2text-generation audio automatic-speech-recognition en dataset:librispeech_asr license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #speech_to_text_transformer #text2text-generation #audio #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

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.


*Result (WER)*:




TAGS
#transformers #pytorch #speech_to_text_transformer #text2text-generation #audio #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #speech_to_text_transformer #text2text-generation #audio #automatic-speech-recognition #en #dataset-librispeech_asr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
