Update README.md

README.md

@@ -31,13 +31,13 @@ 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 Wav2Vec2Tokenizer, Wav2Vec2ForCTC
+ from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
  from datasets import load_dataset
  import soundfile as sf
  import torch
  
- # load model and tokenizer
- tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-large-960h")
+ # load model and processor
+ processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
  model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
  
  # define function to read in sound file
@@ -51,14 +51,14 @@ To transcribe audio files the model can be used as a standalone acoustic model a
  ds = ds.map(map_to_array)
  
  # tokenize
- input_values = tokenizer(ds["speech"][:2], return_tensors="pt", padding="longest").input_values  # Batch size 1
+ input_values = processor(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 = tokenizer.batch_decode(predicted_ids)
+ transcription = processor.batch_decode(predicted_ids)
  ```
  
 ## Evaluation
@@ -67,7 +67,7 @@ This code snippet shows how to evaluate **facebook/wav2vec2-large-960h** on Libr
  
 ```python
 from datasets import load_dataset
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
 import soundfile as sf
 import torch
 from jiwer import wer
@@ -76,7 +76,7 @@ from jiwer import wer
 librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
 
 model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h").to("cuda")
-tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-large-960h")
+processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
 
 def map_to_array(batch):
     speech, _ = sf.read(batch["file"])
@@ -86,12 +86,12 @@ def map_to_array(batch):
 librispeech_eval = librispeech_eval.map(map_to_array)
 
 def map_to_pred(batch):
-    input_values = tokenizer(batch["speech"], return_tensors="pt", padding="longest").input_values
+    input_values = processor(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 = tokenizer.batch_decode(predicted_ids)
+    transcription = processor.batch_decode(predicted_ids)
     batch["transcription"] = transcription
     return batch