Compare commits

..

No commits in common. "22aad52d435eb6dbaf354bdad9b0da84ce7d6156" and "f1f16470cc0c1718bc528eea4f2a9afdccd2f65e" have entirely different histories.

3 changed files with 27 additions and 47 deletions

1
.gitattributes vendored
View File

@ -15,4 +15,3 @@
*.pt filter=lfs diff=lfs merge=lfs -text *.pt filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text *.pth filter=lfs diff=lfs merge=lfs -text
*.msgpack filter=lfs diff=lfs merge=lfs -text *.msgpack filter=lfs diff=lfs merge=lfs -text
model.safetensors filter=lfs diff=lfs merge=lfs -text

View File

@ -5,44 +5,12 @@ datasets:
tags: tags:
- audio - audio
- automatic-speech-recognition - automatic-speech-recognition
- hf-asr-leaderboard
license: apache-2.0 license: apache-2.0
widget: widget:
- example_title: Librispeech sample 1 - label: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2 - label: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
model-index:
- name: wav2vec2-base-960h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 3.4
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 8.6
--- ---
# Wav2Vec2-Base-960h # Wav2Vec2-Base-960h
@ -68,26 +36,34 @@ The original model can be found under https://github.com/pytorch/fairseq/tree/ma
To transcribe audio files the model can be used as a standalone acoustic model as follows: To transcribe audio files the model can be used as a standalone acoustic model as follows:
```python ```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2ForCTC
from datasets import load_dataset from datasets import load_dataset
import soundfile as sf
import torch import torch
# load model and tokenizer # load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
# define function to read in sound file
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
# load dummy dataset and read soundfiles # load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)
# tokenize # tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values # Batch size 1 input_values = tokenizer(ds["speech"][:2], return_tensors="pt", padding="longest").input_values # Batch size 1
# retrieve logits # retrieve logits
logits = model(input_values).logits logits = model(input_values).logits
# take argmax and decode # take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1) predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids) transcription = tokenizer.batch_decode(predicted_ids)
``` ```
## Evaluation ## Evaluation
@ -96,7 +72,8 @@ To transcribe audio files the model can be used as a standalone acoustic model a
```python ```python
from datasets import load_dataset from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
import soundfile as sf
import torch import torch
from jiwer import wer from jiwer import wer
@ -104,19 +81,26 @@ from jiwer import wer
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h")
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): def map_to_pred(batch):
input_values = processor(batch["audio"]["array"], return_tensors="pt", padding="longest").input_values input_values = tokenizer(batch["speech"], return_tensors="pt", padding="longest").input_values
with torch.no_grad(): with torch.no_grad():
logits = model(input_values.to("cuda")).logits logits = model(input_values.to("cuda")).logits
predicted_ids = torch.argmax(logits, dim=-1) predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids) transcription = tokenizer.batch_decode(predicted_ids)
batch["transcription"] = transcription batch["transcription"] = transcription
return batch return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["audio"]) result = librispeech_eval.map(map_to_pred, batched=True, batch_size=1, remove_columns=["speech"])
print("WER:", wer(result["text"], result["transcription"])) print("WER:", wer(result["text"], result["transcription"]))
``` ```

BIN
model.safetensors (Stored with Git LFS)

Binary file not shown.