Spaces:
Runtime error
Runtime error
File size: 5,669 Bytes
c7756c2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 |
# 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",
}
|