codingutf8 2021 the huggingface inc team 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 orglicenseslicense2 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 convert unispeech checkpoint import argparse import json import os import fairseq import torch from fairseq data import dictionary from transformers import unispeechconfig unispeechforctc unispeechforpretraining wav2vec2featureextractor wav2vec2phonemectctokenizer wav2vec2processor logging logging setverbosityinfo logger logging getloggername mapping postextractproj featureprojection projection encoder posconv 0 encoder posconvembed conv selfattn kproj encoder layers attention kproj selfattn vproj encoder layers attention vproj selfattn qproj encoder layers attention qproj selfattn outproj encoder layers attention outproj selfattnlayernorm encoder layers layernorm fc1 encoder layers feedforward intermediatedense fc2 encoder layers feedforward outputdense finallayernorm encoder layers finallayernorm encoder layernorm encoder layernorm w2vmodel layernorm featureprojection layernorm quantizer weightproj quantizer weightproj quantizer vars quantizer codevectors projectq projectq finalproj projecthid w2vencoder proj ctcproj maskemb maskedspecembed toplevelkeys ctcproj quantizer weightproj quantizer codevectors projectq projecthid def setrecursivelyhfpointer key value fullname weighttype isfinetuned for attribute in key split if isfinetuned if attribute in quantizer projectq projecthid those layers are only relevant for pretraining and should be dropped return if attribute ctcproj we should rename ctcproj to lmhead for finetuned phoneme models attribute lmhead hfpointer getattrhfpointer attribute if weighttype is not none hfshape getattrhfpointer weighttype shape else hfshape hfpointer shape assert hfshape value shape fshape of hf key weighttype if weighttype is not none else is hfshape but should be f value shape for fullname if weighttype weight hfpointer weight data value elif weighttype weightg hfpointer weightg data value elif weighttype weightv hfpointer weightv data value elif weighttype bias hfpointer bias data value else hfpointer data value logger infofkey weighttype if weighttype is not none else was initialized from fullname def recursivelyloadweightsfairseqmodel hfmodel isfinetuned unusedweights fairseqdict fairseqmodel statedict featureextractor hfmodel unispeech featureextractor for name value in fairseqdict items isused false if convlayers in name loadconvlayer name value featureextractor unusedweights hfmodel config featextractnorm group isused true else for key mappedkey in mapping items mappedkey unispeech mappedkey if mappedkey not in toplevelkeys else mappedkey if key in name or key splitw2vmodel 1 name split 0 isused true if in mappedkey layerindex name splitkey0 split 2 mappedkey mappedkey replace layerindex if weightg in name weighttype weightg elif weightv in name weighttype weightv elif bias in name weighttype bias elif weight in name todo don t match quantizer weightproj weighttype weight else weighttype none setrecursivelyhfmodel mappedkey value name weighttype isfinetuned continue if not isused unusedweights appendname logger warningfunused weights unusedweights def loadconvlayerfullname value featureextractor unusedweights usegroupnorm name fullname splitconvlayers 1 items name split layerid intitems0 typeid intitems1 if typeid 0 if bias in name assert value shape featureextractor convlayerslayerid conv bias data shape ffullname has size value shape but f featureextractor convlayerslayerid conv bias data shape was found featureextractor convlayerslayerid conv bias data value logger infoffeat extract conv layer layerid was initialized from fullname elif weight in name assert value shape featureextractor convlayerslayerid conv weight data shape ffullname has size value shape but f featureextractor convlayerslayerid conv weight data shape was found featureextractor convlayerslayerid conv weight data value logger infoffeat extract conv layer layerid was initialized from fullname elif typeid 2 and not usegroupnorm or typeid 2 and layerid 0 and usegroupnorm if bias in name assert value shape featureextractor convlayerslayerid layernorm bias data shape ffullname has size value shape but featureextractorlayerid layernorm bias data shape was found featureextractor convlayerslayerid layernorm bias data value logger infoffeat extract layer norm weight of layer layerid was initialized from fullname elif weight in name assert value shape featureextractor convlayerslayerid layernorm weight data shape ffullname has size value shape but f featureextractorlayerid layernorm weight data shape was found featureextractor convlayerslayerid layernorm weight data value logger infoffeat extract layer norm weight of layer layerid was initialized from fullname else unusedweights appendfullname torch nograd def convertunispeechcheckpoint checkpointpath pytorchdumpfolderpath configpathnone dictpathnone isfinetunedtrue if configpath is not none config unispeechconfig frompretrainedconfigpath else config unispeechconfig if isfinetuned if dictpath targetdict dictionary loadfromjsondictpath important change bos pad token id since ctc symbol is pad and not s as in fairseq config bostokenid targetdict padindex config padtokenid targetdict bosindex config eostokenid targetdict eosindex config vocabsize lentargetdict symbols vocabpath os path joinpytorchdumpfolderpath vocab json if not os path isdirpytorchdumpfolderpath logger errorpytorchdumpfolderpath should be a directory formatpytorchdumpfolderpath return os makedirspytorchdumpfolderpath existoktrue vocabdict targetdict indices fairseq has the pad and s switched vocabdictpad 42 vocabdicts 43 with openvocabpath w encodingutf8 as vocabhandle json dumpvocabdict vocabhandle tokenizer wav2vec2phonemectctokenizer vocabpath unktokentargetdict unkword padtokentargetdict padword bostokentargetdict bosword eostokentargetdict eosword worddelimitertoken dolowercasefalse returnattentionmask true if config featextractnorm layer else false featureextractor wav2vec2featureextractor featuresize1 samplingrate16000 paddingvalue0 donormalizetrue returnattentionmaskreturnattentionmask processor wav2vec2processorfeatureextractorfeatureextractor tokenizertokenizer processor savepretrainedpytorchdumpfolderpath hfunispeech unispeechforctcconfig else hfunispeech unispeechforpretrainingconfig if isfinetuned model fairseq checkpointutils loadmodelensembleandtask checkpointpath argoverridesdata joindictpath split 1 w2vpath checkpointpath else model fairseq checkpointutils loadmodelensembleandtaskcheckpointpath model model0 eval recursivelyloadweightsmodel hfunispeech isfinetuned hfunispeech savepretrainedpytorchdumpfolderpath if name main parser argparse argumentparser parser addargumentpytorchdumpfolderpath defaultnone typestr helppath to the output pytorch model parser addargumentcheckpointpath defaultnone typestr helppath to fairseq checkpoint parser addargumentdictpath defaultnone typestr helppath to dict of finetuned model parser addargumentconfigpath defaultnone typestr helppath to hf config json of model to convert parser addargument notfinetuned actionstoretrue helpwhether the model to convert is a finetuned model or not args parser parseargs convertunispeechcheckpoint args checkpointpath args pytorchdumpfolderpath args configpath args dictpath not args notfinetuned coding utf 8 2021 the huggingface inc team 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 convert unispeech checkpoint those layers are only relevant for pretraining and should be dropped we should rename ctc_proj to lm_head for fine tuned phoneme models todo don t match quantizer weight_proj copy paste tweak model s weights to transformers design important change bos pad token id since ctc symbol is pad and not s as in fairseq fairseq has the pad and s switched
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    Wav2Vec2FeatureExtractor,
    Wav2Vec2PhonemeCTCTokenizer,
    Wav2Vec2Processor,
    logging,
)


logging.set_verbosity_info()
logger = logging.get_logger(__name__)

MAPPING = {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
TOP_LEVEL_KEYS = [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def set_recursively(hf_pointer, key, value, full_name, weight_type, is_finetuned):
    for attribute in key.split("."):
        if is_finetuned:
            if attribute in ["quantizer", "project_q", "project_hid"]:
                
                return

            if attribute == "ctc_proj":
                
                attribute = "lm_head"

        hf_pointer = getattr(hf_pointer, attribute)

    if weight_type is not None:
        hf_shape = getattr(hf_pointer, weight_type).shape
    else:
        hf_shape = hf_pointer.shape

    assert hf_shape == value.shape, (
        f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
        f" {value.shape} for {full_name}"
    )

    if weight_type == "weight":
        hf_pointer.weight.data = value
    elif weight_type == "weight_g":
        hf_pointer.weight_g.data = value
    elif weight_type == "weight_v":
        hf_pointer.weight_v.data = value
    elif weight_type == "bias":
        hf_pointer.bias.data = value
    else:
        hf_pointer.data = value

    logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")


def recursively_load_weights(fairseq_model, hf_model, is_finetuned):
    unused_weights = []
    fairseq_dict = fairseq_model.state_dict()

    feature_extractor = hf_model.unispeech.feature_extractor

    for name, value in fairseq_dict.items():
        is_used = False
        if "conv_layers" in name:
            load_conv_layer(
                name,
                value,
                feature_extractor,
                unused_weights,
                hf_model.config.feat_extract_norm == "group",
            )
            is_used = True
        else:
            for key, mapped_key in MAPPING.items():
                mapped_key = "unispeech." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                    is_used = True
                    if "*" in mapped_key:
                        layer_index = name.split(key)[0].split(".")[-2]
                        mapped_key = mapped_key.replace("*", layer_index)
                    if "weight_g" in name:
                        weight_type = "weight_g"
                    elif "weight_v" in name:
                        weight_type = "weight_v"
                    elif "bias" in name:
                        weight_type = "bias"
                    elif "weight" in name:
                        
                        weight_type = "weight"
                    else:
                        weight_type = None
                    set_recursively(hf_model, mapped_key, value, name, weight_type, is_finetuned)
                continue
        if not is_used:
            unused_weights.append(name)

    logger.warning(f"Unused weights: {unused_weights}")


def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm):
    name = full_name.split("conv_layers.")[-1]
    items = name.split(".")
    layer_id = int(items[0])
    type_id = int(items[1])

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
            )
            feature_extractor.conv_layers[layer_id].conv.bias.data = value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
            )
            feature_extractor.conv_layers[layer_id].conv.weight.data = value
            logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                " found."
            )
            feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
            )
            feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
    else:
        unused_weights.append(full_name)


@torch.no_grad()
def convert_unispeech_checkpoint(
    checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True
):
    
    if config_path is not None:
        config = UniSpeechConfig.from_pretrained(config_path)
    else:
        config = UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            target_dict = Dictionary.load_from_json(dict_path)

            
            
            config.bos_token_id = target_dict.pad_index
            config.pad_token_id = target_dict.bos_index
            config.eos_token_id = target_dict.eos_index
            config.vocab_size = len(target_dict.symbols)
            vocab_path = os.path.join(pytorch_dump_folder_path, "vocab.json")
            if not os.path.isdir(pytorch_dump_folder_path):
                logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(pytorch_dump_folder_path))
                return
            os.makedirs(pytorch_dump_folder_path, exist_ok=True)
            vocab_dict = target_dict.indices

            
            vocab_dict["<pad>"] = 42
            vocab_dict["<s>"] = 43
            with open(vocab_path, "w", encoding="utf-8") as vocab_handle:
                json.dump(vocab_dict, vocab_handle)
            tokenizer = Wav2Vec2PhonemeCTCTokenizer(
                vocab_path,
                unk_token=target_dict.unk_word,
                pad_token=target_dict.pad_word,
                bos_token=target_dict.bos_word,
                eos_token=target_dict.eos_word,
                word_delimiter_token="|",
                do_lower_case=False,
            )
            return_attention_mask = True if config.feat_extract_norm == "layer" else False
            feature_extractor = Wav2Vec2FeatureExtractor(
                feature_size=1,
                sampling_rate=16000,
                padding_value=0,
                do_normalize=True,
                return_attention_mask=return_attention_mask,
            )
            processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
            processor.save_pretrained(pytorch_dump_folder_path)

        hf_unispeech = UniSpeechForCTC(config)
    else:
        hf_unispeech = UniSpeechForPreTraining(config)

    if is_finetuned:
        model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1]), "w2v_path": checkpoint_path}
        )
    else:
        model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

    model = model[0].eval()

    recursively_load_weights(model, hf_unispeech, is_finetuned)

    hf_unispeech.save_pretrained(pytorch_dump_folder_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
    parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument(
        "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    args = parser.parse_args()
    convert_unispeech_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )