# Copyright 2022 The HuggingFace Inc. team and the OpenAI team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import hashlib import os import urllib import warnings import torch from torch import nn from tqdm import tqdm from transformers import WhisperConfig, WhisperForConditionalGeneration _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": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt", "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", } def remove_ignore_keys_(state_dict): ignore_keys = ["layers", "blocks"] for k in ignore_keys: state_dict.pop(k, None) WHISPER_MAPPING = { "blocks": "layers", "mlp.0": "fc1", "mlp.2": "fc2", "mlp_ln": "final_layer_norm", ".attn.query": ".self_attn.q_proj", ".attn.key": ".self_attn.k_proj", ".attn.value": ".self_attn.v_proj", ".attn_ln": ".self_attn_layer_norm", ".attn.out": ".self_attn.out_proj", ".cross_attn.query": ".encoder_attn.q_proj", ".cross_attn.key": ".encoder_attn.k_proj", ".cross_attn.value": ".encoder_attn.v_proj", ".cross_attn_ln": ".encoder_attn_layer_norm", ".cross_attn.out": ".encoder_attn.out_proj", "decoder.ln.": "decoder.layer_norm.", "encoder.ln.": "encoder.layer_norm.", "token_embedding": "embed_tokens", "encoder.positional_embedding": "encoder.embed_positions.weight", "decoder.positional_embedding": "decoder.embed_positions.weight", "ln_post": "layer_norm", } def rename_keys(s_dict): keys = list(s_dict.keys()) for key in keys: new_key = key for k, v in WHISPER_MAPPING.items(): if k in key: new_key = new_key.replace(k, v) print(f"{key} -> {new_key}") s_dict[new_key] = s_dict.pop(key) return s_dict def make_linear_from_emb(emb): vocab_size, emb_size = emb.weight.shape lin_layer = nn.Linear(vocab_size, emb_size, bias=False) lin_layer.weight.data = emb.weight.data return lin_layer def _download(url: str, root: str) -> bytes: os.makedirs(root, exist_ok=True) filename = os.path.basename(url) expected_sha256 = url.split("/")[-2] download_target = os.path.join(root, filename) 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): model_bytes = open(download_target, "rb").read() if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: return model_bytes 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 def convert_openai_whisper_to_tfms(checkpoint_path, pytorch_dump_folder_path): if ".pt" not in checkpoint_path: original_checkpoint = _download(_MODELS[checkpoint_path]) else: original_checkpoint = torch.load(checkpoint_path, map_location="cpu") dimensions = original_checkpoint["dims"] state_dict = original_checkpoint["model_state_dict"] proj_out_weights = state_dict["decoder.token_embedding.weight"] remove_ignore_keys_(state_dict) rename_keys(state_dict) tie_embeds = True ffn_dim = state_dict["decoder.layers.0.fc1.weight"].shape[0] config = WhisperConfig( vocab_size=dimensions["n_vocab"], encoder_ffn_dim=ffn_dim, decoder_ffn_dim=ffn_dim, num_mel_bins=dimensions["n_mels"], d_model=dimensions["n_audio_state"], max_target_positions=dimensions["n_text_ctx"], encoder_layers=dimensions["n_audio_layer"], encoder_attention_heads=dimensions["n_audio_head"], decoder_layers=dimensions["n_text_layer"], decoder_attention_heads=dimensions["n_text_state"], max_source_positions=dimensions["n_audio_ctx"], ) model = WhisperForConditionalGeneration(config) missing, unexpected = model.model.load_state_dict(state_dict, strict=False) if len(missing) > 0 and not set(missing) <= { "encoder.embed_positions.weights", "decoder.embed_positions.weights", }: raise ValueError( "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing," f" but all the following weights are missing {missing}" ) if tie_embeds: model.proj_out = make_linear_from_emb(model.model.decoder.embed_tokens) else: model.proj_out.weight.data = proj_out_weights model.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # # Required parameters parser.add_argument("--checkpoint_path", type=str, help="Patht to the downloaded checkpoints") parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") args = parser.parse_args() convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)