def translate(data,
sampling_rate,
pretrained_model_name="facebook/s2t-small-librispeech-asr"):
model = Speech2TextForConditionalGeneration.from_pretrained(
pretrained_model_name)
feature_extractor = Speech2TextFeatureExtractor.from_pretrained(
pretrained_model_name)
tokenizer = Speech2TextTokenizer.from_pretrained(pretrained_model_name)
processor = Speech2TextProcessor(feature_extractor=feature_extractor,
tokenizer=tokenizer)
inputs = processor(data, sampling_rate=sampling_rate, return_tensors="pt")
generated_ids = model.generate(input_ids=inputs["input_features"],
attention_mask=inputs["attention_mask"])
transcription = processor.batch_decode(generated_ids)
return transcription
def get_feature_extractor(self, **kwargs):
return Speech2TextFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
import numpy as np
from scipy.io.wavfile import read
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration, Speech2TextFeatureExtractor, Speech2TextTokenizer
if __name__ == "__main__":
audio_path = "../data/review#1.wav"
samplerate, data = read(audio_path)
data = (data - np.mean(data)) / np.std(data)
print(samplerate, len(data))
model = Speech2TextForConditionalGeneration.from_pretrained(
"facebook/s2t-small-librispeech-asr")
feature_extractor = Speech2TextFeatureExtractor.from_pretrained(
"facebook/s2t-small-librispeech-asr")
tokenizer = Speech2TextTokenizer.from_pretrained(
"facebook/s2t-small-librispeech-asr")
processor = Speech2TextProcessor(feature_extractor=feature_extractor,
tokenizer=tokenizer)
inputs = processor(data, sampling_rate=samplerate, return_tensors="pt")
generated_ids = model.generate(input_ids=inputs["input_features"],
attention_mask=inputs["attention_mask"])
transcription = processor.batch_decode(generated_ids)
print(transcription)