.gitattributes

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

README.md

@@ -5,44 +5,12 @@ datasets:
 tags:
 - audio
 - automatic-speech-recognition
-- hf-asr-leaderboard
 license: apache-2.0
 widget:
-- example_title: Librispeech sample 1
+- label: Librispeech sample 1
   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
-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
@@ -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:
 
 ```python
- from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+ from transformers import Wav2Vec2CTCTokenizer, Wav2Vec2ForCTC
  from datasets import load_dataset
+ import soundfile as sf
  import torch
  
  # 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")
  
+ # 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
  ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+ ds = ds.map(map_to_array)
  
  # 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
  logits = model(input_values).logits
  
  # take argmax and decode
  predicted_ids = torch.argmax(logits, dim=-1)
- transcription = processor.batch_decode(predicted_ids)
+ transcription = tokenizer.batch_decode(predicted_ids)
  ```
  
  ## Evaluation
@@ -96,7 +72,8 @@ To transcribe audio files the model can be used as a standalone acoustic model a
  
 ```python
 from datasets import load_dataset
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+import soundfile as sf
 import torch
 from jiwer import wer
 
@@ -104,19 +81,26 @@ from jiwer import wer
 librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
 
 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):
-    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():
         logits = model(input_values.to("cuda")).logits
 
     predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = processor.batch_decode(predicted_ids)
+    transcription = tokenizer.batch_decode(predicted_ids)
     batch["transcription"] = transcription
     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"]))
 ```