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# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See LICENSE for clarification regarding multiple authors
#
# 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 wave
from typing import List, Tuple
import numpy as np
import sherpa_onnx
from huggingface_hub import hf_hub_download
def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]:
"""
Args:
wave_filename:
Path to a wave file. It should be single channel and each sample should
be 16-bit. Its sample rate does not need to be 16kHz.
Returns:
Return a tuple containing:
- A 1-D array of dtype np.float32 containing the samples, which are
normalized to the range [-1, 1].
- sample rate of the wave file
"""
with wave.open(wave_filename) as f:
assert f.getnchannels() == 1, f.getnchannels()
assert f.getsampwidth() == 2, f.getsampwidth() # it is in bytes
num_samples = f.getnframes()
samples = f.readframes(num_samples)
samples_int16 = np.frombuffer(samples, dtype=np.int16)
samples_float32 = samples_int16.astype(np.float32)
samples_float32 = samples_float32 / 32768
return samples_float32, f.getframerate()
def _get_nn_model_filename(
repo_id: str,
filename: str,
subfolder: str = ".",
) -> str:
nn_model_filename = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
)
return nn_model_filename
def get_speaker_segmentation_model(repo_id) -> List[str]:
assert repo_id in ("pyannote/segmentation-3.0",)
if repo_id == "pyannote/segmentation-3.0":
return _get_nn_model_filename(
repo_id="csukuangfj/sherpa-onnx-pyannote-segmentation-3-0",
filename="model.onnx",
)
def get_speaker_embedding_model(model_name) -> List[str]:
assert (
model_name
in three_d_speaker_embedding_models
+ nemo_speaker_embedding_models
+ wespeaker_embedding_models
)
return _get_nn_model_filename(
repo_id="csukuangfj/speaker-embedding-models",
filename=model_name,
)
def get_speaker_diarization(
segmentation_model: str, embedding_model: str, num_clusters: int, threshold: float
):
segmentation = get_speaker_segmentation_model(segmentation_model)
embedding = get_speaker_embedding_model(embedding_model)
config = sherpa_onnx.OfflineSpeakerDiarizationConfig(
segmentation=sherpa_onnx.OfflineSpeakerSegmentationModelConfig(
pyannote=sherpa_onnx.OfflineSpeakerSegmentationPyannoteModelConfig(
model=segmentation
),
),
embedding=sherpa_onnx.SpeakerEmbeddingExtractorConfig(model=embedding),
clustering=sherpa_onnx.FastClusteringConfig(
num_clusters=num_clusters,
threshold=threshold,
),
min_duration_on=0.3,
min_duration_off=0.5,
)
if not config.validate():
raise RuntimeError(
"Please check your config and make sure all required files exist"
)
return sherpa_onnx.OfflineSpeakerDiarization(config)
pass
speaker_segmentation_models = ["pyannote/segmentation-3.0"]
nemo_speaker_embedding_models = [
"nemo_en_speakerverification_speakernet.onnx",
"nemo_en_titanet_large.onnx",
"nemo_en_titanet_small.onnx",
]
three_d_speaker_embedding_models = [
"3dspeaker_speech_campplus_sv_en_voxceleb_16k.onnx",
"3dspeaker_speech_campplus_sv_zh-cn_16k-common.onnx",
"3dspeaker_speech_campplus_sv_zh_en_16k-common_advanced.onnx",
"3dspeaker_speech_eres2net_base_200k_sv_zh-cn_16k-common.onnx",
"3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx",
"3dspeaker_speech_eres2net_large_sv_zh-cn_3dspeaker_16k.onnx",
"3dspeaker_speech_eres2net_sv_en_voxceleb_16k.onnx",
"3dspeaker_speech_eres2net_sv_zh-cn_16k-common.onnx",
"3dspeaker_speech_eres2netv2_sv_zh-cn_16k-common.onnx",
]
wespeaker_embedding_models = [
"wespeaker_en_voxceleb_CAM++.onnx",
"wespeaker_en_voxceleb_CAM++_LM.onnx",
"wespeaker_en_voxceleb_resnet152_LM.onnx",
"wespeaker_en_voxceleb_resnet221_LM.onnx",
"wespeaker_en_voxceleb_resnet293_LM.onnx",
"wespeaker_en_voxceleb_resnet34.onnx",
"wespeaker_en_voxceleb_resnet34_LM.onnx",
"wespeaker_zh_cnceleb_resnet34.onnx",
"wespeaker_zh_cnceleb_resnet34_LM.onnx",
]
embedding2models = {
"3D-Speaker": three_d_speaker_embedding_models,
"NeMo": nemo_speaker_embedding_models,
"WeSpeaker": wespeaker_embedding_models,
}
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