speaker-diarization-ov

Name: speaker-diarization-ov
Author: FluidInference

license:mit

FluidInference

Other

OTHER

New

10 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

Unknown

Mobile

Laptop

Server

Quick Summary

Pyannote and wespeaker models converted for Speaker diarization and identification for OpenVINO

Code Examples

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

import librosa
import matplotlib.pyplot as plt
import librosa.display
import IPython.display as ipd

sample_file = "tutorials_assets_sample.wav"
audio, sr = librosa.load(sample_file)
waveform = torch.from_numpy(audio[0:160000]).unsqueeze(0).unsqueeze(0)
plt.figure(figsize=(14, 5))
librosa.display.waveshow(audio, sr=sr)

ipd.Audio(sample_file)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
model = core.read_model("pyannote-segmentation.xml")
compiled_model = core.compile_model(model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: waveform})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

python

core = ov.Core()
embedding_openvino_model = core.read_model("pyannote-wespeaker.xml")
embedding_openvino_model.reshape((1, 100, 80))
compiled_model = core.compile_model(embedding_openvino_model, "NPU")  # or "NPU" if supported
input_name = compiled_model.input(0)
output_name = compiled_model.output(0)

results = compiled_model({input_name: torch.zeros((1, 100, 80))})
output = results[output_name]
output.sum(axis=1)

Deploy This Model

Production-ready deployment in minutes

Together.ai

Instant API access to this model

Fastest API

Production-ready inference API. Start free, scale to millions.

Try Free API

Replicate

One-click model deployment

Easiest Setup

Run models in the cloud with simple API. No DevOps required.

Deploy Now

Disclosure: We may earn a commission from these partners. This helps keep LLMYourWay free.