Qwen3 Voice Embedding 12Hz 1.7B EU AI Act Compliance Profile

ECAPA-TDNN Voice Encoder (from Qwen3-TTS 1.7B)

Standalone ECAPA-TDNN voice encoder extracted from Qwen/Qwen3-TTS-12Hz-1.7B-Base. Produces 2048-dimensional x-vector speaker embeddings from audio.

The encoder follows the ECAPA-TDNN architecture (Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification) and uses Res2Net blocks, squeeze-excitation attention, and attentive statistical pooling.

[!WARNING] Only extracted from the *-Base model variant. The *-CustomVoice and *-VoiceDesign variants do NOT support speaker embeddings and will not work. Do not attempt to use them for speaker encoding.

Usage

Recommended: AutoProcessor + AutoModel

import librosa
import torch
from transformers import AutoModel, AutoProcessor

processor = AutoProcessor.from_pretrained(
    "marksverdhei/Qwen3-Voice-Embedding-12Hz-1.7B", trust_remote_code=True,
)
model = AutoModel.from_pretrained(
    "marksverdhei/Qwen3-Voice-Embedding-12Hz-1.7B", trust_remote_code=True,
)
model.eval()

audio, sr = librosa.load("audio.wav", sr=None, mono=True)
inputs = processor(audio, sampling_rate=sr)

with torch.no_grad():
    embedding = model(**inputs).last_hidden_state  # (1, 2048)

Pipeline API

from transformers import pipeline

pipe = pipeline(
    "feature-extraction",
    model="marksverdhei/Qwen3-Voice-Embedding-12Hz-1.7B",
    trust_remote_code=True,
)

# From file path
embedding = pipe("audio.wav")  # list, shape (1, 2048)

# From numpy array
import librosa
audio, sr = librosa.load("audio.wav", sr=None, mono=True)
embedding = pipe(audio, sampling_rate=sr)

Saving & Loading Embeddings

Speaker embeddings can be stored and shared as SafeTensors files.

Save to SafeTensors

import torch
from safetensors.torch import save_file

# embedding: torch.Tensor of shape (2048,) or (1, 2048)
embedding = e

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Tags

Library

Base Model