| from transformers import SeamlessM4Tv2ForSpeechToText,SeamlessM4TTokenizer, SeamlessM4TFeatureExtractor | |
| from numpy import array as np_array,float32 as np_float32 | |
| from pydub import AudioSegment | |
| class INDIC_SEAMLESS: | |
| def __init__(self,lang_conf:dict[str,str],model,device): | |
| self.seamless_model = SeamlessM4Tv2ForSpeechToText.from_pretrained(model).to(device) | |
| self.seamless_processor = SeamlessM4TFeatureExtractor.from_pretrained(model) | |
| self.seamless_tokenizer = SeamlessM4TTokenizer.from_pretrained(model) | |
| self.lang_conf = lang_conf | |
| def speech2translate(self,audio_paths, target_lang): | |
| return self.seamless_tokenizer.batch_decode(self.seamless_model.generate(**self.seamless_processor([np_array(AudioSegment.from_file(path).set_channels(1).set_frame_rate(16000).get_array_of_samples(), dtype=np_float32) / 32768.0 for path in audio_paths], sampling_rate=16000, return_tensors="pt", padding=True).to("cpu"), tgt_lang=self.lang_conf[target_lang]), skip_special_tokens=True) | |