
# Wav2Vec2-Conformer-Large-960h with Rotary Position Embeddings + 4-gram

This model is identical to [Facebook's wav2vec2-conformer-rope-large-960h-ft](https://huggingface.co/facebook/wav2vec2-conformer-rope-large-960h-ft), but is 
augmented with an English 4-gram. The `4-gram.arpa.gz` of [Librispeech's official ngrams](https://www.openslr.org/11) is used.
 
 ## Evaluation
 
 This code snippet shows how to evaluate **patrickvonplaten/wav2vec2-conformer-rope-large-960h-ft-4-gram** on LibriSpeech's "clean" and "other" test data.
 
```python
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torch
from jiwer import wer

model_id = "patrickvonplaten/wav2vec2-conformer-rope-large-960h-ft-4-gram"

librispeech_eval = load_dataset("librispeech_asr", "other", split="test")

model = AutoModelForCTC.from_pretrained(model_id).to("cuda")
processor = AutoProcessor.from_pretrained(model_id)

def map_to_pred(batch):
    inputs = processor(batch["audio"]["array"], sampling_rate=16_000, return_tensors="pt")

    inputs = {k: v.to("cuda") for k,v in inputs.items()}

    with torch.no_grad():
        logits = model(**inputs).logits

    transcription = processor.batch_decode(logits.cpu().numpy()).text[0]
    batch["transcription"] = transcription
    return batch

result = librispeech_eval.map(map_to_pred, remove_columns=["audio"])

print(wer(result["text"], result["transcription"]))
```

*Result (WER)*:

| "clean" | "other" |
|---|---|
| 1.88 | 3.57 |
{"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-conformer-rope-large-960h-ft-4-gram", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "LibriSpeech (clean)", "type": "librispeech_asr", "config": "clean", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 1.88, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "LibriSpeech (other)", "type": "librispeech_asr", "config": "other", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 3.57, "name": "Test WER"}]}]}]}
transformers pytorch wav2vec2-conformer automatic-speech-recognition speech audio hf-asr-leaderboard en dataset:librispeech_asr license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

Wav2Vec2-Conformer-Large-960h with Rotary Position Embeddings + 4-gram
======================================================================


This model is identical to Facebook's wav2vec2-conformer-rope-large-960h-ft, but is
augmented with an English 4-gram. The 'URL' of Librispeech's official ngrams is used.


Evaluation
----------


This code snippet shows how to evaluate patrickvonplaten/wav2vec2-conformer-rope-large-960h-ft-4-gram on LibriSpeech's "clean" and "other" test data.


*Result (WER)*:




TAGS
#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #wav2vec2-conformer #automatic-speech-recognition #speech #audio #hf-asr-leaderboard #en #dataset-librispeech_asr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
