def test_save_load_pretrained_additional_features(self):
processor = Speech2TextProcessor(
tokenizer=self.get_tokenizer(),
feature_extractor=self.get_feature_extractor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)",
eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(
do_normalize=False, padding_value=1.0)
processor = Speech2TextProcessor.from_pretrained(self.tmpdirname,
bos_token="(BOS)",
eos_token="(EOS)",
do_normalize=False,
padding_value=1.0)
self.assertEqual(processor.tokenizer.get_vocab(),
tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, Speech2TextTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(),
feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor,
Speech2TextFeatureExtractor)
def s2t_predictions(audio_file):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
audio_array = s2t_audio_to_array(audio_file)
model = Speech2TextForConditionalGeneration.from_pretrained(
"facebook/s2t-small-librispeech-asr").to(device).eval()
processor = Speech2TextProcessor.from_pretrained(
"facebook/s2t-small-librispeech-asr", do_upper_case=True)
features = processor(audio_array, sampling_rate=16000, return_tensors="pt")
input_features = features.input_features.to(device)
attention_mask = features.attention_mask.to(device)
gen_tokens = model.generate(input_ids=input_features)
text = processor.batch_decode(gen_tokens, skip_special_tokens=True)
return text
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = Speech2TextProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = Speech2TextProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, Speech2TextTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, Speech2TextFeatureExtractor)
def default_processor(self):
return Speech2TextProcessor.from_pretrained(
"facebook/s2t-small-librispeech-asr")