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import moviepy.editor as mp
from pyannote.audio import Pipeline
import torch
import torchaudio
from pyannote.audio import Pipeline
from pyannote.core import Segment
from pyannote.audio import Model
import os
def extract_audio_from_video(video_path):
video = mp.VideoFileClip(video_path)
audio_path = video_path.rsplit('.', 1)[0] + '.wav'
video.audio.write_audiofile(audio_path)
return audio_path
def diarize_speakers(audio_path):
hf_token = os.environ.get("py_annote_hf_token")
if not hf_token:
raise ValueError("py_annote_hf_token environment variable is not set. Please check your Hugging Face Space's Variables and secrets section.")
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", use_auth_token=hf_token)
diarization = pipeline(audio_path)
return diarization
def get_speaker_embeddings(audio_path, diarization, model_name="pyannote/embedding"):
hf_token = os.environ.get("py_annote_hf_token")
if not hf_token:
raise ValueError("py_annote_hf_token environment variable is not set. Please check your Hugging Face Space's Variables and secrets section.")
model = Model.from_pretrained(model_name, use_auth_token=hf_token)
model.eval() # Set the model to evaluation mode
waveform, sample_rate = torchaudio.load(audio_path)
embeddings = []
for turn, _, speaker in diarization.itertracks(yield_label=True):
start_frame = int(turn.start * sample_rate)
end_frame = int(turn.end * sample_rate)
segment = waveform[:, start_frame:end_frame]
if segment.shape[1] == 0:
continue
# Ensure the segment is long enough (at least 1 second)
if segment.shape[1] < sample_rate:
padding = torch.zeros(1, sample_rate - segment.shape[1])
segment = torch.cat([segment, padding], dim=1)
with torch.no_grad():
embedding = model(segment) # Pass the tensor directly, not a dictionary
embeddings.append({"time": turn.start, "embedding": embedding.squeeze().cpu().numpy(), "speaker": speaker})
return embeddings
def align_voice_embeddings(voice_embeddings, frame_count, fps):
import numpy as np
aligned_embeddings = []
current_embedding_index = 0
for frame in range(frame_count):
frame_time = frame / fps
while (current_embedding_index < len(voice_embeddings) - 1 and
voice_embeddings[current_embedding_index + 1]["time"] <= frame_time):
current_embedding_index += 1
aligned_embeddings.append(voice_embeddings[current_embedding_index]["embedding"])
return np.array(aligned_embeddings) |