hr16/multilingual_spin

Multilingual speaker-invariant speech content encoder based on SPIN method taken from FreeSVC, converted to transformers library format. Usage:

from transformers import AutoProcessor, HubertModel
feature_extractor = AutoFeatureExtractor.from_pretrained("hr16/multilingual_spin")
model = HubertModel.from_pretrained("hr16/multilingual_spin")

audio_input = torch.randn(16000)  # 1 second of audio at 16kHz
inputs = feature_extractor(audio_input, sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

print(outputs.last_hidden_state.shape)

Conversion code:

import re
import torch
def convert_hubert_s3prl_to_transformers(model, checkpoint_path: str):
    # 1. Load checkpoint
    state_dict = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
    if "model" in state_dict:
        state_dict = state_dict["model"]
    elif "state_dict" in state_dict:
        state_dict = state_dict["state_dict"]

    hf_state_dict = {}

    # 2. Map weights
    for key, value in state_dict.items():
        # Skip unused pretraining components
        if (
            key.startswith("pred_head")
            or key.startswith("loss_module")
            or "label_embs_concat" in key
        ):
            continue

        # Mask embedding
        if key == "encoder.model.mask_emb":
            hf_state_dict["masked_spec_embed"] = value
            continue

        # Feature extractor group norm / conv layers
        if "feature_extractor.conv_layers" in key:
            # e.g.: encoder.model.feature_extractor.conv_layers.0.0.weight -> feature_extractor.conv_layers.0.conv.weight
            # e.g.: encoder.model.feature_extractor.conv_layers.0.2.weight -> feature_extractor.conv_layers.0.layer_norm.weight
            match = re.search(r"conv_layers\.(\d+)\.(\d+)\.(weight|bias)", key)
            if match:
                layer_idx, sub_idx, param_name = match.groups()
                if sub_idx == "0":
                    hf_key = f"feature_extractor.conv_layers.{layer_idx}.conv.{param_name}"
                elif sub_idx == "2":
                    hf_key = f"feature_extractor.conv_layers.{layer_idx}.layer_norm.{param_name}"
                hf_state_dict[hf_key] = value
            continue

        # Feature projection layer norm
        if key in ("encoder.model.layer_norm.weight", "encoder.model.layer_norm.bias"):
            param_name = key.split(".")[-1]
            hf_state_dict[f"feature_projection.layer_norm.{param_name}"] = value
            continue

        # Feature projection linear layer
        if "post_extract_proj" in key:
            param_name = key.split(".")[-1]
            hf_state_dict[f"feature_projection.projection.{param_name}"] = value
            continue

        # Positional Convolution Embedding
        if "pos_conv.0" in key:
            param_name = key.split(".")[-1]
            # Handles weight_v, weight_g, bias
            hf_state_dict[f"encoder.pos_conv_embed.conv.{param_name}"] = value
            continue

        # Encoder pre-LayerNorm
        if key in (
            "encoder.model.encoder.layer_norm.weight",
            "encoder.model.encoder.layer_norm.bias",
        ):
            param_name = key.split(".")[-1]
            hf_state_dict[f"encoder.layer_norm.{param_name}"] = value
            continue

        # Encoder Transformer Layers
        if "encoder.model.encoder.layers." in key:
            # Map layer components
            m = re.match(
                r"encoder\.model\.encoder\.layers\.(\d+)\.(.+)", key
            )
            if m:
                layer_idx, rest = m.groups()
                prefix = f"encoder.layers.{layer_idx}"

                if "self_attn." in rest:
                    # self_attn.{q,k,v,out}_proj.{weight,bias}
                    attn_component = rest.replace("self_attn.", "attention.")
                    hf_state_dict[f"{prefix}.{attn_component}"] = value
                elif "self_attn_layer_norm." in rest:
                    param_name = rest.split(".")[-1]
                    hf_state_dict[f"{prefix}.layer_norm.{param_name}"] = value
                elif "fc1." in rest:
                    param_name = rest.split(".")[-1]
                    hf_state_dict[
                        f"{prefix}.feed_forward.intermediate_dense.{param_name}"
                    ] = value
                elif "fc2." in rest:
                    param_name = rest.split(".")[-1]
                    hf_state_dict[
                        f"{prefix}.feed_forward.output_dense.{param_name}"
                    ] = value
                elif "final_layer_norm." in rest:
                    param_name = rest.split(".")[-1]
                    hf_state_dict[f"{prefix}.final_layer_norm.{param_name}"] = value
            continue

        print(f"Unmapped key: {key}")
    model.load_state_dict(hf_state_dict)
    return model

from transformers import HubertModel
orig_hubert = HubertModel.from_pretrained("facebook/hubert-base-ls960")
spin = convert_hubert_s3prl_to_transformers(HubertModel.from_pretrained("facebook/hubert-base-ls960"), "spin.ckpt")
spin.push_to_hub("hr16/multilingual_spin")

State dict mean-square differences:

masked_spec_embed diff: 0.0
feature_extractor.conv_layers.0.conv.weight diff: 0.0
feature_extractor.conv_layers.0.layer_norm.weight diff: 0.0
feature_extractor.conv_layers.0.layer_norm.bias diff: 0.0
feature_extractor.conv_layers.1.conv.weight diff: 0.0
feature_extractor.conv_layers.2.conv.weight diff: 0.0
feature_extractor.conv_layers.3.conv.weight diff: 0.0
feature_extractor.conv_layers.4.conv.weight diff: 0.0
feature_extractor.conv_layers.5.conv.weight diff: 0.0
feature_extractor.conv_layers.6.conv.weight diff: 0.0
feature_projection.layer_norm.weight diff: 0.0
feature_projection.layer_norm.bias diff: 0.0
feature_projection.projection.weight diff: 0.0
feature_projection.projection.bias diff: 0.0
encoder.pos_conv_embed.conv.bias diff: 0.0
encoder.pos_conv_embed.conv.parametrizations.weight.original0 diff: 0.0
encoder.pos_conv_embed.conv.parametrizations.weight.original1 diff: 0.0
encoder.layer_norm.weight diff: 0.0
encoder.layer_norm.bias diff: 0.0
encoder.layers.0.attention.k_proj.weight diff: 0.0
encoder.layers.0.attention.k_proj.bias diff: 0.0
encoder.layers.0.attention.v_proj.weight diff: 0.0
encoder.layers.0.attention.v_proj.bias diff: 0.0
encoder.layers.0.attention.q_proj.weight diff: 0.0
encoder.layers.0.attention.q_proj.bias diff: 0.0
encoder.layers.0.attention.out_proj.weight diff: 0.0
encoder.layers.0.attention.out_proj.bias diff: 0.0
encoder.layers.0.layer_norm.weight diff: 0.0
encoder.layers.0.layer_norm.bias diff: 0.0
encoder.layers.0.feed_forward.intermediate_dense.weight diff: 0.0
encoder.layers.0.feed_forward.intermediate_dense.bias diff: 0.0
encoder.layers.0.feed_forward.output_dense.weight diff: 0.0
encoder.layers.0.feed_forward.output_dense.bias diff: 0.0
encoder.layers.0.final_layer_norm.weight diff: 0.0
encoder.layers.0.final_layer_norm.bias diff: 0.0
encoder.layers.1.attention.k_proj.weight diff: 0.0
encoder.layers.1.attention.k_proj.bias diff: 0.0
encoder.layers.1.attention.v_proj.weight diff: 0.0
encoder.layers.1.attention.v_proj.bias diff: 0.0
encoder.layers.1.attention.q_proj.weight diff: 0.0
encoder.layers.1.attention.q_proj.bias diff: 0.0
encoder.layers.1.attention.out_proj.weight diff: 0.0
encoder.layers.1.attention.out_proj.bias diff: 0.0
encoder.layers.1.layer_norm.weight diff: 0.0
encoder.layers.1.layer_norm.bias diff: 0.0
encoder.layers.1.feed_forward.intermediate_dense.weight diff: 0.0
encoder.layers.1.feed_forward.intermediate_dense.bias diff: 0.0
encoder.layers.1.feed_forward.output_dense.weight diff: 0.0
encoder.layers.1.feed_forward.output_dense.bias diff: 0.0
encoder.layers.1.final_layer_norm.weight diff: 0.0
encoder.layers.1.final_layer_norm.bias diff: 0.0
encoder.layers.2.attention.k_proj.weight diff: 0.0
encoder.layers.2.attention.k_proj.bias diff: 0.0
encoder.layers.2.attention.v_proj.weight diff: 0.0
encoder.layers.2.attention.v_proj.bias diff: 0.0
encoder.layers.2.attention.q_proj.weight diff: 0.0
encoder.layers.2.attention.q_proj.bias diff: 0.0
encoder.layers.2.attention.out_proj.weight diff: 0.0
encoder.layers.2.attention.out_proj.bias diff: 0.0
encoder.layers.2.layer_norm.weight diff: 0.0
encoder.layers.2.layer_norm.bias diff: 0.0
encoder.layers.2.feed_forward.intermediate_dense.weight diff: 0.0
encoder.layers.2.feed_forward.intermediate_dense.bias diff: 0.0
encoder.layers.2.feed_forward.output_dense.weight diff: 0.0
encoder.layers.2.feed_forward.output_dense.bias diff: 0.0
encoder.layers.2.final_layer_norm.weight diff: 0.0
encoder.layers.2.final_layer_norm.bias diff: 0.0
encoder.layers.3.attention.k_proj.weight diff: 0.0
encoder.layers.3.attention.k_proj.bias diff: 0.0
encoder.layers.3.attention.v_proj.weight diff: 0.0
encoder.layers.3.attention.v_proj.bias diff: 0.0
encoder.layers.3.attention.q_proj.weight diff: 0.0
encoder.layers.3.attention.q_proj.bias diff: 0.0
encoder.layers.3.attention.out_proj.weight diff: 0.0
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encoder.layers.3.layer_norm.weight diff: 0.0
encoder.layers.3.layer_norm.bias diff: 0.0
encoder.layers.3.feed_forward.intermediate_dense.weight diff: 0.0
encoder.layers.3.feed_forward.intermediate_dense.bias diff: 0.0
encoder.layers.3.feed_forward.output_dense.weight diff: 0.0
encoder.layers.3.feed_forward.output_dense.bias diff: 0.0
encoder.layers.3.final_layer_norm.weight diff: 0.0
encoder.layers.3.final_layer_norm.bias diff: 0.0
encoder.layers.4.attention.k_proj.weight diff: 0.0
encoder.layers.4.attention.k_proj.bias diff: 0.0
encoder.layers.4.attention.v_proj.weight diff: 0.0
encoder.layers.4.attention.v_proj.bias diff: 0.0
encoder.layers.4.attention.q_proj.weight diff: 0.0
encoder.layers.4.attention.q_proj.bias diff: 0.0
encoder.layers.4.attention.out_proj.weight diff: 0.0
encoder.layers.4.attention.out_proj.bias diff: 0.0
encoder.layers.4.layer_norm.weight diff: 0.0
encoder.layers.4.layer_norm.bias diff: 0.0
encoder.layers.4.feed_forward.intermediate_dense.weight diff: 0.0
encoder.layers.4.feed_forward.intermediate_dense.bias diff: 0.0
encoder.layers.4.feed_forward.output_dense.weight diff: 0.0
encoder.layers.4.feed_forward.output_dense.bias diff: 0.0
encoder.layers.4.final_layer_norm.weight diff: 0.0
encoder.layers.4.final_layer_norm.bias diff: 0.0
encoder.layers.5.attention.k_proj.weight diff: 0.0
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encoder.layers.5.attention.v_proj.weight diff: 0.0
encoder.layers.5.attention.v_proj.bias diff: 0.0
encoder.layers.5.attention.q_proj.weight diff: 0.0
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encoder.layers.6.attention.out_proj.weight diff: 0.0
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encoder.layers.6.layer_norm.weight diff: 0.0
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encoder.layers.6.feed_forward.output_dense.weight diff: 0.0
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encoder.layers.7.attention.v_proj.weight diff: 0.0
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encoder.layers.7.attention.q_proj.weight diff: 0.0
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encoder.layers.7.attention.out_proj.weight diff: 0.0
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encoder.layers.7.layer_norm.weight diff: 0.0
encoder.layers.7.layer_norm.bias diff: 0.0
encoder.layers.7.feed_forward.intermediate_dense.weight diff: 0.0
encoder.layers.7.feed_forward.intermediate_dense.bias diff: 0.0
encoder.layers.7.feed_forward.output_dense.weight diff: 0.0
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