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# import torch
# import torchaudio
# from fairseq2.assets import InProcAssetMetadataProvider, asset_store
# from fairseq2.data import Collater, SequenceData
# from fairseq2.data.audio import (
# AudioDecoder,
# WaveformToFbankConverter,
# WaveformToFbankOutput,
# )
# from fairseq2.generation import SequenceGeneratorOptions
# from fairseq2.memory import MemoryBlock
# from fairseq2.typing import DataType, Device
# from huggingface_hub import snapshot_download
# from seamless_communication.inference import BatchedSpeechOutput, Translator
# from seamless_communication.models.generator.loader import load_pretssel_vocoder_model
# from seamless_communication.models.unity import (
# UnitTokenizer,
# load_gcmvn_stats,
# load_unity_text_tokenizer,
# load_unity_unit_tokenizer,
# )
# from torch.nn import Module
# class PretsselGenerator(Module):
# def __init__(
# self,
# pretssel_name_or_card: str,
# unit_tokenizer: UnitTokenizer,
# device: Device,
# dtype: DataType = torch.float16,
# ):
# super().__init__()
# # Load the model.
# if device == torch.device("cpu"):
# dtype = torch.float32
# self.device = device
# self.dtype = dtype
# self.pretssel_model = load_pretssel_vocoder_model(
# pretssel_name_or_card,
# device=device,
# dtype=dtype,
# )
# self.pretssel_model.eval()
# vocoder_model_card = asset_store.retrieve_card(pretssel_name_or_card)
# self.output_sample_rate = vocoder_model_card.field("sample_rate").as_(int)
# self.unit_tokenizer = unit_tokenizer
# self.unit_collate = Collater(pad_value=unit_tokenizer.vocab_info.pad_idx)
# self.duration_collate = Collater(pad_value=0)
# @torch.inference_mode()
# def predict(
# self,
# units: list[list[int]],
# tgt_lang: str,
# prosody_encoder_input: SequenceData,
# ) -> BatchedSpeechOutput:
# audio_wavs = []
# unit_eos_token = torch.tensor(
# [self.unit_tokenizer.vocab_info.eos_idx],
# device=self.device,
# )
# prosody_input_seqs = prosody_encoder_input["seqs"]
# prosody_input_lens = prosody_encoder_input["seq_lens"]
# for i, u in enumerate(units):
# unit = torch.tensor(u).to(unit_eos_token)
# # adjust the control symbols for the embedding
# unit += 4
# unit = torch.cat([unit, unit_eos_token], dim=0)
# unit, duration = torch.unique_consecutive(unit, return_counts=True)
# # adjust for the last eos token
# duration[-1] = 0
# duration *= 2
# prosody_input_seq = prosody_input_seqs[i][: prosody_input_lens[i]]
# audio_wav = self.pretssel_model(
# unit,
# tgt_lang,
# prosody_input_seq,
# durations=duration.unsqueeze(0),
# )
# audio_wavs.append(audio_wav)
# return BatchedSpeechOutput(
# units=units,
# audio_wavs=audio_wavs,
# sample_rate=self.output_sample_rate,
# )
LANGUAGE_CODE_TO_NAME = {
"afr": "Afrikaans",
"amh": "Amharic",
"arb": "Modern Standard Arabic",
"ary": "Moroccan Arabic",
"arz": "Egyptian Arabic",
"asm": "Assamese",
"ast": "Asturian",
"azj": "North Azerbaijani",
"bel": "Belarusian",
"ben": "Bengali",
"bos": "Bosnian",
"bul": "Bulgarian",
"cat": "Catalan",
"ceb": "Cebuano",
"ces": "Czech",
"ckb": "Central Kurdish",
"cmn": "Mandarin Chinese",
"cym": "Welsh",
"dan": "Danish",
"deu": "German",
"ell": "Greek",
"eng": "English",
"est": "Estonian",
"eus": "Basque",
"fin": "Finnish",
"fra": "French",
"gaz": "West Central Oromo",
"gle": "Irish",
"glg": "Galician",
"guj": "Gujarati",
"heb": "Hebrew",
"hin": "Hindi",
"hrv": "Croatian",
"hun": "Hungarian",
"hye": "Armenian",
"ibo": "Igbo",
"ind": "Indonesian",
"isl": "Icelandic",
"ita": "Italian",
"jav": "Javanese",
"jpn": "Japanese",
"kam": "Kamba",
"kan": "Kannada",
"kat": "Georgian",
"kaz": "Kazakh",
"kea": "Kabuverdianu",
"khk": "Halh Mongolian",
"khm": "Khmer",
"kir": "Kyrgyz",
"kor": "Korean",
"lao": "Lao",
"lit": "Lithuanian",
"ltz": "Luxembourgish",
"lug": "Ganda",
"luo": "Luo",
"lvs": "Standard Latvian",
"mai": "Maithili",
"mal": "Malayalam",
"mar": "Marathi",
"mkd": "Macedonian",
"mlt": "Maltese",
"mni": "Meitei",
"mya": "Burmese",
"nld": "Dutch",
"nno": "Norwegian Nynorsk",
"nob": "Norwegian Bokm\u00e5l",
"npi": "Nepali",
"nya": "Nyanja",
"oci": "Occitan",
"ory": "Odia",
"pan": "Punjabi",
"pbt": "Southern Pashto",
"pes": "Western Persian",
"pol": "Polish",
"por": "Portuguese",
"ron": "Romanian",
"rus": "Russian",
"slk": "Slovak",
"slv": "Slovenian",
"sna": "Shona",
"snd": "Sindhi",
"som": "Somali",
"spa": "Spanish",
"srp": "Serbian",
"swe": "Swedish",
"swh": "Swahili",
"tam": "Tamil",
"tel": "Telugu",
"tgk": "Tajik",
"tgl": "Tagalog",
"tha": "Thai",
"tur": "Turkish",
"ukr": "Ukrainian",
"urd": "Urdu",
"uzn": "Northern Uzbek",
"vie": "Vietnamese",
"xho": "Xhosa",
"yor": "Yoruba",
"yue": "Cantonese",
"zlm": "Colloquial Malay",
"zsm": "Standard Malay",
"zul": "Zulu",
}