attach_speaker_embedding_s2s.py

import subprocess
from os.path import join as p_join
from typing import Optional

import librosa
from librosa import feature
import numpy as np
from torch import nn


import os
from os.path import expanduser

import shutil
import torch
from soundfile import LibsndfileError
from datasets import load_dataset, DatasetDict, Audio

direction = os.getenv("DIRECTION", "enA-jaA")
sides = set(direction.split("-"))
dataset_id = os.getenv("DATASET_ID", 0)
num_proc = int(os.getenv("NUM_PROC", 1))
hf_org = os.getenv("HF_ORG", "asahi417")
hf_dataset = f"seamless-align-{direction}"
dataset = load_dataset(f"{hf_org}/{hf_dataset}", f"subset_{dataset_id}", split="train")
audio_loader = Audio()

checkpoint_url = "https://huggingface.co./datasets/asahi417/experiment-speaker-embedding/resolve/main/meta_voice_speaker_encoder.pt"
model_weight = p_join(os.path.expanduser('~'), ".cache", "experiment_speaker_embedding", "meta_voice_speaker_encoder.pt")


def wget(url: str, output_file: Optional[str] = None):
    os.makedirs(os.path.dirname(output_file), exist_ok=True)
    subprocess.run(["wget", url, "-O", output_file])
    if not os.path.exists(output_file):
        raise ValueError(f"failed to download {url}")


class MetaVoiceSE(nn.Module):

    mel_window_length = 25
    mel_window_step = 10
    mel_n_channels = 40
    sampling_rate = 16000
    partials_n_frames = 160
    model_hidden_size = 256
    model_embedding_size = 256
    model_num_layers = 3

    def __init__(self):
        super().__init__()
        if not os.path.exists(model_weight):
            wget(checkpoint_url, model_weight)
        # Define the network
        self.lstm = nn.LSTM(self.mel_n_channels, self.model_hidden_size, self.model_num_layers, batch_first=True)
        self.linear = nn.Linear(self.model_hidden_size, self.model_embedding_size)
        self.relu = nn.ReLU()
        # Load weight
        self.load_state_dict(torch.load(model_weight, map_location="cpu")["model_state"], strict=False)
        # Get the target device
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.to(self.device)
        self.eval()

    def compute_partial_slices(self, n_samples: int, rate, min_coverage):
        # Compute how many frames separate two partial utterances
        samples_per_frame = int((self.sampling_rate * self.mel_window_step / 1000))
        n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
        frame_step = int(np.round((self.sampling_rate / rate) / samples_per_frame))
        # Compute the slices
        wav_slices, mel_slices = [], []
        steps = max(1, n_frames - self.partials_n_frames + frame_step + 1)
        for i in range(0, steps, frame_step):
            mel_range = np.array([i, i + self.partials_n_frames])
            wav_range = mel_range * samples_per_frame
            mel_slices.append(slice(*mel_range))
            wav_slices.append(slice(*wav_range))
        # Evaluate whether extra padding is warranted or not
        last_wav_range = wav_slices[-1]
        coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start)
        if coverage < min_coverage and len(mel_slices) > 1:
            return wav_slices[:-1], mel_slices[:-1]
        return wav_slices, mel_slices

    def get_speaker_embedding(self,
                              wav: np.ndarray,
                              sampling_rate: Optional[int] = None,
                              rate: float = 1.3,
                              min_coverage: float = 0.75) -> np.ndarray:
        if sampling_rate != self.sampling_rate:
            wav = librosa.resample(wav, orig_sr=sampling_rate, target_sr=self.sampling_rate)
        wav, _ = librosa.effects.trim(wav, top_db=20)
        wav_slices, mel_slices = self.compute_partial_slices(len(wav), rate, min_coverage)
        max_wave_length = wav_slices[-1].stop
        if max_wave_length >= len(wav):
            wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
        # Wav -> Mel spectrogram
        frames = feature.melspectrogram(
            y=wav,
            sr=self.sampling_rate,
            n_fft=int(self.sampling_rate * self.mel_window_length / 1000),
            hop_length=int(self.sampling_rate * self.mel_window_step / 1000),
            n_mels=self.mel_n_channels,
        )
        mel = frames.astype(np.float32).T
        mel = np.array([mel[s] for s in mel_slices])
        # inference
        with torch.no_grad():
            mel = torch.from_numpy(mel).to(self.device)
            _, (hidden, _) = self.lstm(mel)
            embeds_raw = self.relu(self.linear(hidden[-1]))
            partial_embeds = embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
        partial_embeds = partial_embeds.cpu().numpy()
        raw_embed = np.mean(partial_embeds, axis=0)
        return raw_embed / np.linalg.norm(raw_embed, 2)


speaker_embedder = MetaVoiceSE()


def error_file(example):
    for side in sides:
        try:
            audio_loader.decode_example(example[f"{side}.audio"])
        except LibsndfileError:
            return False
    return True


print(f"Num examples: {len(dataset)}")
for s in sides:
    dataset = dataset.cast_column(f"{s}.audio", Audio(decode=False))
dataset = dataset.filter(error_file, num_proc=num_proc, desc="drop broken audio")
for s in sides:
    dataset = dataset.cast_column(f"{s}.audio", Audio())
print(f"Num examples (after filtering): {len(dataset)}")


def speaker_embedding(example):
    for side in sides:
        example[f"{side}.audio.speaker_embedding"] = speaker_embedder.get_speaker_embedding(
            example[f"{side}.audio"]["array"], example[f"{side}.audio"]["sampling_rate"]
        )
    return example


dataset = dataset.map(
    function=speaker_embedding,
    remove_columns=[f"{s}.audio" for s in sides] + [f"{s}.url" for s in sides] + [f"{s}.duration_start" for s in sides] + [f"{s}.duration_end" for s in sides],
    num_proc=num_proc,
    desc="attach speaker embedding dataset"
)
DatasetDict({"train": dataset}).push_to_hub(f"{hf_org}/{hf_dataset}.speaker-embedding.metavoice", config_name=f"subset_{dataset_id}")
cache_dir = f"{expanduser('~')}/.cache/huggingface/datasets/{hf_org}___{hf_dataset}/subset_{dataset_id}"
if os.path.exists(cache_dir):
    shutil.rmtree(cache_dir)