import hashlib import io import os import urllib import warnings from typing import List, Optional, Union import torch from tqdm import tqdm from .audio import load_audio, pad_or_trim, log_mel_spectrogram from .model import ModelDimensions, Whisper from .streaming_model import StreamingWhisper from .version import __version__ _MODELS = { "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", "large": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", "large-v3-turbo": "https://openaipublic.azureedge.net/main/whisper/models/aff26ae408abcba5fbf8813c21e62b0941638c5f6eebfb145be0c9839262a19a/large-v3-turbo.pt", "turbo": "https://openaipublic.azureedge.net/main/whisper/models/aff26ae408abcba5fbf8813c21e62b0941638c5f6eebfb145be0c9839262a19a/large-v3-turbo.pt", } _STREAMING_MODELS = { "base": { "300": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_base_LIBRI-960-ALIGNED_32_full_streaming_eot_fixed_timings_LR-1e-05_r32_g15_eg1_top5_full-streamTrue_random-orderFalse_fraction0.25/checkpoint/checkpoint-epoch=0009.pt", "200": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_base_LIBRI-960-ALIGNED_32_full_streaming_eot_fixed_timings_LR-1e-05_r32_g10_eg2_top5_full-streamTrue_random-orderFalse_fraction0.1/checkpoint/checkpoint-epoch=0009.pt", "100": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_base_LIBRI-960-ALIGNED_32_full_streaming_eot_fixed_timings_LR-1e-05_r32_g5_eg5_top5_full-streamTrue_random-orderFalse_fraction0.05/checkpoint/checkpoint-epoch=0009.pt", "40": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_base_LIBRI-960-ALIGNED_32_full_streaming_eot_fixed_timings_LR-1e-05_r32_g2_eg14_top5_full-streamTrue_random-orderFalse_fraction0.02/checkpoint/checkpoint-epoch=0006.pt", }, "small": { "1000": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_small_LIBRI-960-ALIGNED_16_full_streaming_eot_fixed_timings_LR-1e-05_r32_g50_eg0_top5_full-streamTrue_random-orderFalse_fraction0.4/checkpoint/checkpoint-epoch=0009.pt", "300": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_small_LIBRI-960-ALIGNED_16_full_streaming_eot_fixed_timings_LR-1e-05_r32_g15_eg1_top5_full-streamTrue_random-orderFalse_fraction0.25/checkpoint/checkpoint-epoch=0009.pt", "200": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_small_LIBRI-960-ALIGNED_16_full_streaming_eot_fixed_timings_LR-1e-05_r32_g10_eg2_top5_full-streamTrue_random-orderFalse_fraction0.1/checkpoint/checkpoint-epoch=0009.pt", "100": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_small_LIBRI-960-ALIGNED_16_full_streaming_eot_fixed_timings_LR-1e-05_r32_g5_eg5_top5_full-streamTrue_random-orderFalse_fraction0.05/checkpoint/checkpoint-epoch=0009.pt", "40": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_small_LIBRI-960-ALIGNED_16_full_streaming_eot_fixed_timings_LR-1e-05_r32_g2_eg14_top5_full-streamTrue_random-orderFalse_fraction0.02/checkpoint/checkpoint-epoch=0009.pt", }, "large-v2": { "1000": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_large-v2_LIBRI-960-ALIGNED_4_full_streaming_eot_fixed_timings_LR-1e-05_r4_g50_eg0_top5_full-streamTrue_random-orderFalse_fraction0.3/checkpoint/checkpoint-epoch=0002.pt", "300": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_large-v2_LIBRI-960-ALIGNED_4_full_streaming_eot_fixed_timings_LR-1e-05_r4_g15_eg1_top5_full-streamTrue_random-orderFalse_fraction0.1/checkpoint/checkpoint-epoch=0002.pt", "200": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_large-v2_LIBRI-960-ALIGNED_4_full_streaming_eot_fixed_timings_LR-1e-05_r4_g10_eg2_top5_full-streamTrue_random-orderFalse_fraction0.07/checkpoint/checkpoint-epoch=0002.pt", "100": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_large-v2_LIBRI-960-ALIGNED_4_full_streaming_eot_fixed_timings_LR-1e-05_r4_g5_eg5_top5_full-streamTrue_random-orderFalse_fraction0.03/checkpoint/checkpoint-epoch=0002.pt", "40": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_large-v2_LIBRI-960-ALIGNED_4_full_streaming_eot_fixed_timings_LR-1e-05_r4_g2_eg14_top5_full-streamTrue_random-orderFalse_fraction0.01/checkpoint/checkpoint-epoch=0002.pt", "300-multi": "/mlspeech/data/tomer/streaming_whisper/models/ckpts/LoRA_streamed_whisper_large-v2_LIBRI-BLEND-ALIGNED_4_full_streaming_eot_fixed_timings_LR-1e-05_r4_g15_eg1_top5_full-streamTrue_random-orderFalse_fraction0.05/checkpoint/checkpoint-epoch=0001.pt", } } _STREAMING_MODELS_HF = { "base": { "300": "base_300.pt", "200": "base_200.pt", "100": "base_100.pt", "40": "base_40.pt", }, "small": { "1000": "small_1000.pt", "300": "small_300.pt", "200": "small_200.pt", "100": "small_100.pt", "40": "small_40.pt", }, "large-v2": { "1000": "large-v2_1000.pt", "300": "large-v2_300.pt", "200": "large-v2_200.pt", "100": "large-v2_100.pt", "40": "large-v2_40.pt", "300-multi": "large-v2_300_multi.pt", } } # base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are # highly correlated to the word-level timing, i.e. the alignment between audio and text tokens. _ALIGNMENT_HEADS = { "tiny.en": b"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00", "tiny": b"ABzY8bu8Lr0{>%RKn9Fp%m@SkK7Kt=7ytkO", "base.en": b"ABzY8;40c<0{>%RzzG;p*o+Vo09|#PsxSZm00", "base": b"ABzY8KQ!870{>%RzyTQH3`Q^yNP!>##QT-?_)10{>%RpeA61k&I|OI3I$65C{;;pbCHh0B{qLQ;+}v00", "small": b"ABzY8DmU6=0{>%Rpa?J`kvJ6qF(V^F86#Xh7JUGMK}P%R7%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9", "large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj", "large-v3": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00", "large": b"ABzY8gWO1E0{>%R7(9S+Kn!D~%ngiGaR?*L!iJG9p-nab0JQ=-{D1-g00", "large-v3-turbo": b"ABzY8j^C+e0{>%RARaKHP%t(lGR*)0g!tONPyhe`", "turbo": b"ABzY8j^C+e0{>%RARaKHP%t(lGR*)0g!tONPyhe`", } def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: os.makedirs(root, exist_ok=True) expected_sha256 = url.split("/")[-2] download_target = os.path.join(root, os.path.basename(url)) if os.path.exists(download_target) and not os.path.isfile(download_target): raise RuntimeError(f"{download_target} exists and is not a regular file") if os.path.isfile(download_target): with open(download_target, "rb") as f: model_bytes = f.read() if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: return model_bytes if in_memory else download_target else: warnings.warn( f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" ) with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: with tqdm( total=int(source.info().get("Content-Length")), ncols=80, unit="iB", unit_scale=True, unit_divisor=1024, ) as loop: while True: buffer = source.read(8192) if not buffer: break output.write(buffer) loop.update(len(buffer)) model_bytes = open(download_target, "rb").read() if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: raise RuntimeError( "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." ) return model_bytes if in_memory else download_target def available_models() -> List[str]: """Returns the names of available models""" return list(_MODELS.keys()) def load_model( name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False, ) -> Whisper: """ Load a Whisper ASR model Parameters ---------- name : str one of the official model names listed by `whisper.available_models()`, or path to a model checkpoint containing the model dimensions and the model state_dict. device : Union[str, torch.device] the PyTorch device to put the model into download_root: str path to download the model files; by default, it uses "~/.cache/whisper" in_memory: bool whether to preload the model weights into host memory Returns ------- model : Whisper The Whisper ASR model instance """ if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" if download_root is None: default = os.path.join(os.path.expanduser("~"), ".cache") download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper") if name in _MODELS: checkpoint_file = _download(_MODELS[name], download_root, in_memory) alignment_heads = _ALIGNMENT_HEADS[name] elif os.path.isfile(name): checkpoint_file = open(name, "rb").read() if in_memory else name alignment_heads = None else: raise RuntimeError( f"Model {name} not found; available models = {available_models()}" ) with ( io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb") ) as fp: checkpoint = torch.load(fp, map_location=device) del checkpoint_file dims = ModelDimensions(**checkpoint["dims"]) model = Whisper(dims) model.load_state_dict(checkpoint["model_state_dict"]) if alignment_heads is not None: model.set_alignment_heads(alignment_heads) return model.to(device) def load_streaming_model( name: str, advisor_ckpt_path: str = None, ft_model_ckpt_path: str = None, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False, cache_gran: bool = True, gran: int = 15, rank: int = 8, extra_gran_blocks: int = 0, n_advisor_class: int = 4, **kwargs: any ) -> StreamingWhisper: """ Load a StreamingWhisper ASR model Parameters ---------- name : str one of the official model names listed by `whisper.available_models()`, or path to a model checkpoint containing the model dimensions and the model state_dict. device : Union[str, torch.device] the PyTorch device to put the model into download_root: str path to download the model files; by default, it uses "~/.cache/whisper" in_memory: bool whether to preload the model weights into host memory Returns ------- model : Whisper The Whisper ASR model instance """ if ft_model_ckpt_path is None: if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" if download_root is None: default = os.path.join(os.path.expanduser("~"), ".cache") download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper") if name in _MODELS: checkpoint_file = _download(_MODELS[name], download_root, in_memory) alignment_heads = _ALIGNMENT_HEADS[name] elif os.path.isfile(name): checkpoint_file = open(name, "rb").read() if in_memory else name alignment_heads = None else: raise RuntimeError( f"Model {name} not found; available models = {available_models()}" ) with ( io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb") ) as fp: checkpoint = torch.load(fp, map_location=device) del checkpoint_file else: checkpoint = torch.load(ft_model_ckpt_path, weights_only=False) decoder_advisor_chkpt = torch.load(advisor_ckpt_path, weights_only=False) if advisor_ckpt_path is not None else {"state_dict": {}} advisor_state_dict = {k: v for k, v in decoder_advisor_chkpt["state_dict"].items() if "decoder_advisor" in k} whisper_dict = checkpoint["model_state_dict"] if "model_state_dict" in checkpoint.keys() else checkpoint["state_dict"] whisper_dict = {k.replace("weight", "base_layer.weight") if "attn." in k and "weight" in k else k.replace("bias", "base_layer.bias") if "attn." in k and "bias" in k else k: v for k, v in whisper_dict.items()} streaming_whisper_state_dict = {**advisor_state_dict, **whisper_dict} dims = ModelDimensions(**checkpoint["dims"]) model = StreamingWhisper(dims, cache_gran=cache_gran, gran=gran, rank=rank, extra_gran_blocks=extra_gran_blocks, n_advisor_class=n_advisor_class, **kwargs) model.load_state_dict(streaming_whisper_state_dict, strict=False) # for n, p in model.named_parameters(): # print(n, p) if ft_model_ckpt_path is None and alignment_heads is not None: model.set_alignment_heads(alignment_heads) return model.to(device) def load_streaming_model_correct( name: str, gran: int = 300, multilingual: bool = False, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False, ) -> StreamingWhisper: subname = (str(gran) + '-multi') if multilingual else str(gran) from huggingface_hub import hf_hub_download hf_token = os.environ.get("HF_TOKEN") try: ckpt_path = hf_hub_download(repo_id="MLSpeech/causal-whisper", filename=_STREAMING_MODELS_HF[name][subname], repo_type="model", token=hf_token) except KeyError as e: print(f"Streaming model with the next configs: size {name}, multilingual: {multilingual} and chunk size: {gran} is not available.") checkpoint = torch.load(ckpt_path, weights_only=False) dims = ModelDimensions(**checkpoint["dims"]) model = StreamingWhisper(dims, gran=checkpoint['cfg']['gran'], rank=checkpoint['cfg']['rank'], extra_gran_blocks=checkpoint['cfg']['extra_gran_blocks']) model.load_state_dict(checkpoint['state_dict'], strict=False) return model.to(device)