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vap API

maai.models.vap

VapGPT

Bases: Module

Voice Activity Projection (VAP) core model using GPT architecture.

This model processes audio features using a GPT-based architecture to predict future voice activity, which can be used for turn-taking predictions in spoken dialogue systems.

Source code in src/maai/models/vap.py 
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class VapGPT(nn.Module):
    """Voice Activity Projection (VAP) core model using GPT architecture.

    This model processes audio features using a GPT-based architecture
    to predict future voice activity, which can be used for turn-taking
    predictions in spoken dialogue systems.
    """


    BINS_P_NOW = [0, 1]
    BINS_PFUTURE = [2, 3]

    def __init__(self, conf: Optional[VapConfig] = None):
        """Initialize the VapGPT model.

        Args:
            conf (Optional[VapConfig]): Configuration object for the model.
                If None, default VapConfig is used.
        """
        super().__init__()
        if conf is None:
            conf = VapConfig()
        self.conf = conf
        self.sample_rate = conf.sample_rate
        self.frame_hz = conf.frame_hz

        self.temp_elapse_time = []

        # Single channel
        self.ar_channel = GPT(
            dim=conf.dim,
            dff_k=3,
            num_layers=conf.channel_layers,
            num_heads=conf.num_heads,
            dropout=conf.dropout,
            context_limit=conf.context_limit,
        )

        # Cross channel
        self.ar = GPTStereo(
            dim=conf.dim,
            dff_k=3,
            num_layers=conf.cross_layers,
            num_heads=conf.num_heads,
            dropout=conf.dropout,
            context_limit=conf.context_limit,
        )

        self.objective = ObjectiveVAP(bin_times=conf.bin_times, frame_hz=conf.frame_hz)

        # Outputs
        # Voice activity objective -> x1, x2 -> logits ->  BCE
        self.va_classifier = nn.Linear(conf.dim, 1)

        if self.conf.lid_classify == 1:
            self.lid_classifier = nn.Linear(conf.dim, conf.lid_classify_num_class)

        elif self.conf.lid_classify == 2:
            self.lid_classifier_middle = nn.Linear(conf.dim*2, conf.lid_classify_num_class)

        if self.conf.lang_cond == 1:
            self.lang_condition = nn.Linear(conf.lid_classify_num_class, conf.dim)

        self.vap_head = nn.Linear(conf.dim, self.objective.n_classes)

    def load_encoder(self, cpc_model):
        """Load and build the audio encoders for both speakers.

        Args:
            cpc_model: Pre-trained CPC model to be used as feature extractor.
        """
        self.encoder1 = build_audio_encoder(self.conf, cpc_model=cpc_model)
        self.encoder1 = self.encoder1.eval()
        self.encoder2 = build_audio_encoder(self.conf, cpc_model=cpc_model)
        self.encoder2 = self.encoder2.eval()

        encoder_dim = getattr(self.encoder1, "output_dim", self.conf.dim)
        if encoder_dim != self.conf.dim:
            self.decrease_dimension = nn.Linear(encoder_dim, self.conf.dim)

        if self.conf.freeze_encoder == 1:
            print('freeze encoder')
            self.encoder1.freeze()
            self.encoder2.freeze()

    @property
    def horizon_time(self):
        """Get the horizon time for the projection in seconds.

        Returns:
            float: Horizon time for the objective.
        """
        return self.objective.horizon_time

    def encode_audio(self, audio1: torch.Tensor, audio2: torch.Tensor) -> Tuple[Tensor, Tensor]:
        """Encode the raw audio inputs into feature representations.

        Args:
            audio1 (torch.Tensor): Audio waveform for speaker 1.
            audio2 (torch.Tensor): Audio waveform for speaker 2.

        Returns:
            Tuple[Tensor, Tensor]: Encoded features for speaker 1 and speaker 2.
        """


        x1 = self.encoder1(audio1)  # speaker 1
        x2 = self.encoder2(audio2)  # speaker 2

        if hasattr(self, "decrease_dimension"):
            x1 = self.decrease_dimension(x1)
            x2 = self.decrease_dimension(x2)

        return x1, x2

    def vad_loss(self, vad_output, vad):
        """Compute the Voice Activity Detection (VAD) loss.

        Args:
            vad_output: Predicted VAD logits.
            vad: Ground truth VAD labels.

        Returns:
            Tensor: Binary cross-entropy loss between predictions and targets.
        """
        return F.binary_cross_entropy_with_logits(vad_output, vad)

    def forward(
        self,
        x1: Tensor,
        x2: Tensor,
        cache: Optional[dict] = None,
    ) -> Tuple[dict, dict]:
        """
        Forward pass for the VapGPT model.

        Args:
            x1 (Tensor): Input audio embedded tensor for speaker 1.
            x2 (Tensor): Input audio embedded tensor for speaker 2.
            cache (dict, optional): Cache of past keys/values.

        Returns:
            Tuple[dict, dict]: Model outputs and updated cache.
        """

        if cache is None:
            cache = {}

        o1 = self.ar_channel(x1, past_kv=cache.get("ar1"))
        o2 = self.ar_channel(x2, past_kv=cache.get("ar2"))
        out = self.ar(
            o1["x"],
            o2["x"],
            past_kv1=cache.get("cross1"),
            past_kv2=cache.get("cross2"),
            past_kv1_c=cache.get("cross1_c"),
            past_kv2_c=cache.get("cross2_c"),
        )

        new_cache = {
            "ar1": (o1["past_k"], o1["past_v"]),
            "ar2": (o2["past_k"], o2["past_v"]),
            "cross1": (out["past_k1"], out["past_v1"]),
            "cross2": (out["past_k2"], out["past_v2"]),
            "cross1_c": (out["past_k1_c"], out["past_v1_c"]),
            "cross2_c": (out["past_k2_c"], out["past_v2_c"]),
        }

        # Outputs
        vad1 = self.va_classifier(o1["x"])
        vad2 = self.va_classifier(o2["x"])
        logits = self.vap_head(out["x"])

        probs = logits.softmax(dim=-1)

        p_bins_tensor = self.objective.probs_speaker_bin_aggregate(
            probs, from_bin=0, to_bin=self.objective.n_bins - 1
        )

        # 話者次元は正規化せず、該当ビン（p_now / p_future と同じ範囲）の値を足したもの
        i0, i1 = self.BINS_P_NOW[0], self.BINS_P_NOW[-1]
        j0, j1 = self.BINS_PFUTURE[0], self.BINS_PFUTURE[-1]
        p_bins_now_t = p_bins_tensor[:, :, :, i0 : i1 + 1].sum(dim=-1)
        p_bins_future_t = p_bins_tensor[:, :, :, j0 : j1 + 1].sum(dim=-1)

        p_now = self.objective.probs_next_speaker_aggregate(
            probs,
            from_bin=self.BINS_P_NOW[0],
            to_bin=self.BINS_P_NOW[-1],
        )

        p_future = self.objective.probs_next_speaker_aggregate(
            probs,
            from_bin=self.BINS_PFUTURE[0],
            to_bin=self.BINS_PFUTURE[1],
        )

        # Get back to the CPU（ビン合計は最大 2 なので 2 で割り各話者 [0, 1] に正規化）
        p_bins = p_bins_tensor.to("cpu").tolist()[0][-1]
        p_bins_now = (p_bins_now_t * 0.5).to("cpu").tolist()[0][-1]
        p_bins_future = (p_bins_future_t * 0.5).to("cpu").tolist()[0][-1]
        p_now = p_now.to("cpu").tolist()[0][-1]
        p_future = p_future.to("cpu").tolist()[0][-1]

        vad1 = vad1.sigmoid().to("cpu").tolist()[0][-1][0]
        vad2 = vad2.sigmoid().to("cpu").tolist()[0][-1][0]

        ret = {
            "p_now": p_now,
            "p_future": p_future,
            "vad": [vad1, vad2],
            "p_bins": p_bins,
            "p_bins_now": p_bins_now,
            "p_bins_future": p_bins_future,
        }

        return ret, new_cache

horizon_time property

Get the horizon time for the projection in seconds.

Returns:

Name Type Description
float

Horizon time for the objective.

__init__(conf=None)

Initialize the VapGPT model.

Parameters:

Name Type Description Default
conf Optional[VapConfig]

Configuration object for the model. If None, default VapConfig is used.

 None
Source code in src/maai/models/vap.py 
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def __init__(self, conf: Optional[VapConfig] = None):
    """Initialize the VapGPT model.

    Args:
        conf (Optional[VapConfig]): Configuration object for the model.
            If None, default VapConfig is used.
    """
    super().__init__()
    if conf is None:
        conf = VapConfig()
    self.conf = conf
    self.sample_rate = conf.sample_rate
    self.frame_hz = conf.frame_hz

    self.temp_elapse_time = []

    # Single channel
    self.ar_channel = GPT(
        dim=conf.dim,
        dff_k=3,
        num_layers=conf.channel_layers,
        num_heads=conf.num_heads,
        dropout=conf.dropout,
        context_limit=conf.context_limit,
    )

    # Cross channel
    self.ar = GPTStereo(
        dim=conf.dim,
        dff_k=3,
        num_layers=conf.cross_layers,
        num_heads=conf.num_heads,
        dropout=conf.dropout,
        context_limit=conf.context_limit,
    )

    self.objective = ObjectiveVAP(bin_times=conf.bin_times, frame_hz=conf.frame_hz)

    # Outputs
    # Voice activity objective -> x1, x2 -> logits ->  BCE
    self.va_classifier = nn.Linear(conf.dim, 1)

    if self.conf.lid_classify == 1:
        self.lid_classifier = nn.Linear(conf.dim, conf.lid_classify_num_class)

    elif self.conf.lid_classify == 2:
        self.lid_classifier_middle = nn.Linear(conf.dim*2, conf.lid_classify_num_class)

    if self.conf.lang_cond == 1:
        self.lang_condition = nn.Linear(conf.lid_classify_num_class, conf.dim)

    self.vap_head = nn.Linear(conf.dim, self.objective.n_classes)

encode_audio(audio1, audio2)

Encode the raw audio inputs into feature representations.

Parameters:

Name Type Description Default
audio1 Tensor

Audio waveform for speaker 1.

 required
audio2 Tensor

Audio waveform for speaker 2.

 required

Returns:

Type Description
Tuple[Tensor, Tensor]

Tuple[Tensor, Tensor]: Encoded features for speaker 1 and speaker 2.
Source code in src/maai/models/vap.py 
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def encode_audio(self, audio1: torch.Tensor, audio2: torch.Tensor) -> Tuple[Tensor, Tensor]:
    """Encode the raw audio inputs into feature representations.

    Args:
        audio1 (torch.Tensor): Audio waveform for speaker 1.
        audio2 (torch.Tensor): Audio waveform for speaker 2.

    Returns:
        Tuple[Tensor, Tensor]: Encoded features for speaker 1 and speaker 2.
    """


    x1 = self.encoder1(audio1)  # speaker 1
    x2 = self.encoder2(audio2)  # speaker 2

    if hasattr(self, "decrease_dimension"):
        x1 = self.decrease_dimension(x1)
        x2 = self.decrease_dimension(x2)

    return x1, x2

forward(x1, x2, cache=None)

Forward pass for the VapGPT model.

Parameters:

Name Type Description Default
x1 Tensor

Input audio embedded tensor for speaker 1.

 required
x2 Tensor

Input audio embedded tensor for speaker 2.

 required
cache dict

Cache of past keys/values.

 None

Returns:

Type Description
Tuple[dict, dict]

Tuple[dict, dict]: Model outputs and updated cache.
Source code in src/maai/models/vap.py 
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def forward(
    self,
    x1: Tensor,
    x2: Tensor,
    cache: Optional[dict] = None,
) -> Tuple[dict, dict]:
    """
    Forward pass for the VapGPT model.

    Args:
        x1 (Tensor): Input audio embedded tensor for speaker 1.
        x2 (Tensor): Input audio embedded tensor for speaker 2.
        cache (dict, optional): Cache of past keys/values.

    Returns:
        Tuple[dict, dict]: Model outputs and updated cache.
    """

    if cache is None:
        cache = {}

    o1 = self.ar_channel(x1, past_kv=cache.get("ar1"))
    o2 = self.ar_channel(x2, past_kv=cache.get("ar2"))
    out = self.ar(
        o1["x"],
        o2["x"],
        past_kv1=cache.get("cross1"),
        past_kv2=cache.get("cross2"),
        past_kv1_c=cache.get("cross1_c"),
        past_kv2_c=cache.get("cross2_c"),
    )

    new_cache = {
        "ar1": (o1["past_k"], o1["past_v"]),
        "ar2": (o2["past_k"], o2["past_v"]),
        "cross1": (out["past_k1"], out["past_v1"]),
        "cross2": (out["past_k2"], out["past_v2"]),
        "cross1_c": (out["past_k1_c"], out["past_v1_c"]),
        "cross2_c": (out["past_k2_c"], out["past_v2_c"]),
    }

    # Outputs
    vad1 = self.va_classifier(o1["x"])
    vad2 = self.va_classifier(o2["x"])
    logits = self.vap_head(out["x"])

    probs = logits.softmax(dim=-1)

    p_bins_tensor = self.objective.probs_speaker_bin_aggregate(
        probs, from_bin=0, to_bin=self.objective.n_bins - 1
    )

    # 話者次元は正規化せず、該当ビン（p_now / p_future と同じ範囲）の値を足したもの
    i0, i1 = self.BINS_P_NOW[0], self.BINS_P_NOW[-1]
    j0, j1 = self.BINS_PFUTURE[0], self.BINS_PFUTURE[-1]
    p_bins_now_t = p_bins_tensor[:, :, :, i0 : i1 + 1].sum(dim=-1)
    p_bins_future_t = p_bins_tensor[:, :, :, j0 : j1 + 1].sum(dim=-1)

    p_now = self.objective.probs_next_speaker_aggregate(
        probs,
        from_bin=self.BINS_P_NOW[0],
        to_bin=self.BINS_P_NOW[-1],
    )

    p_future = self.objective.probs_next_speaker_aggregate(
        probs,
        from_bin=self.BINS_PFUTURE[0],
        to_bin=self.BINS_PFUTURE[1],
    )

    # Get back to the CPU（ビン合計は最大 2 なので 2 で割り各話者 [0, 1] に正規化）
    p_bins = p_bins_tensor.to("cpu").tolist()[0][-1]
    p_bins_now = (p_bins_now_t * 0.5).to("cpu").tolist()[0][-1]
    p_bins_future = (p_bins_future_t * 0.5).to("cpu").tolist()[0][-1]
    p_now = p_now.to("cpu").tolist()[0][-1]
    p_future = p_future.to("cpu").tolist()[0][-1]

    vad1 = vad1.sigmoid().to("cpu").tolist()[0][-1][0]
    vad2 = vad2.sigmoid().to("cpu").tolist()[0][-1][0]

    ret = {
        "p_now": p_now,
        "p_future": p_future,
        "vad": [vad1, vad2],
        "p_bins": p_bins,
        "p_bins_now": p_bins_now,
        "p_bins_future": p_bins_future,
    }

    return ret, new_cache

load_encoder(cpc_model)

Load and build the audio encoders for both speakers.

Parameters:

Name Type Description Default
cpc_model

Pre-trained CPC model to be used as feature extractor.

 required
Source code in src/maai/models/vap.py 
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def load_encoder(self, cpc_model):
    """Load and build the audio encoders for both speakers.

    Args:
        cpc_model: Pre-trained CPC model to be used as feature extractor.
    """
    self.encoder1 = build_audio_encoder(self.conf, cpc_model=cpc_model)
    self.encoder1 = self.encoder1.eval()
    self.encoder2 = build_audio_encoder(self.conf, cpc_model=cpc_model)
    self.encoder2 = self.encoder2.eval()

    encoder_dim = getattr(self.encoder1, "output_dim", self.conf.dim)
    if encoder_dim != self.conf.dim:
        self.decrease_dimension = nn.Linear(encoder_dim, self.conf.dim)

    if self.conf.freeze_encoder == 1:
        print('freeze encoder')
        self.encoder1.freeze()
        self.encoder2.freeze()

vad_loss(vad_output, vad)

Compute the Voice Activity Detection (VAD) loss.

Parameters:

Name Type Description Default
vad_output

Predicted VAD logits.

 required
vad

Ground truth VAD labels.

 required

Returns:

Name Type Description
Tensor

Binary cross-entropy loss between predictions and targets.
Source code in src/maai/models/vap.py 
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def vad_loss(self, vad_output, vad):
    """Compute the Voice Activity Detection (VAD) loss.

    Args:
        vad_output: Predicted VAD logits.
        vad: Ground truth VAD labels.

    Returns:
        Tensor: Binary cross-entropy loss between predictions and targets.
    """
    return F.binary_cross_entropy_with_logits(vad_output, vad)