Create audio.py

audio.py

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+import base64
+import gzip
+from dataclasses import dataclass
+from typing import Dict, Iterable, Optional, List
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+from subprocess import CalledProcessError, run, Popen, PIPE
+
+import os
+from functools import lru_cache
+from typing import Optional, Union
+
+def exact_div(x, y):
+    assert x % y == 0
+    return x // y
+
+# hard-coded audio hyperparameters
+SAMPLE_RATE = 16000
+N_FFT = 400
+N_MELS = 80
+HOP_LENGTH = 160
+CHUNK_LENGTH = 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000 samples in a 30-second chunk
+N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH)  # 3000 frames in a mel spectrogram input
+
+N_SAMPLES_PER_TOKEN = HOP_LENGTH * 2  # the initial convolutions has stride 2
+FRAMES_PER_SECOND = exact_div(SAMPLE_RATE, HOP_LENGTH)  # 10ms per audio frame
+TOKENS_PER_SECOND = exact_div(SAMPLE_RATE, N_SAMPLES_PER_TOKEN)  # 20ms per audio token
+
+
+
+def get_T_after_cnn(L_in, dilation=1):
+    for (padding, kernel_size, stride) in eval("[(1,3,1)] + [(1,3,2)] "):
+        L_out = L_in + 2 * padding - dilation * (kernel_size - 1) - 1
+        L_out = 1 + L_out // stride
+        L_in = L_out
+    return L_out
+
+def load_bytesio_audio(content, sr: int = SAMPLE_RATE):
+    cmd = [
+        "ffmpeg",
+        "-nostdin",
+        "-threads", "0",
+        "-i", "pipe:",
+        "-f", "s16le",
+        "-ac", "1",
+        "-acodec", "pcm_s16le",
+        "-ar", str(sr),
+        "pipe:"
+    ]
+    p = Popen(cmd, stdin=PIPE, stdout=PIPE, stderr=PIPE, bufsize=-1)
+    out, _ = p.communicate(input=content)
+    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
+
+def load_audio(file: str, sr: int = SAMPLE_RATE):
+    """
+    Open an audio file and read as mono waveform, resampling as necessary
+    Parameters
+    ----------
+    file: str
+        The audio file to open
+    sr: int
+        The sample rate to resample the audio if necessary
+    Returns
+    -------
+    A NumPy array containing the audio waveform, in float32 dtype.
+    """
+
+    # This launches a subprocess to decode audio while down-mixing
+    # and resampling as necessary.  Requires the ffmpeg CLI in PATH.
+    # fmt: off
+    cmd = [
+        "ffmpeg",
+        "-nostdin",
+        "-threads", "0",
+        "-i", file,
+        "-f", "s16le",
+        "-ac", "1",
+        "-acodec", "pcm_s16le",
+        "-ar", str(sr),
+        "-"
+    ]
+    # fmt: on
+    try:
+        out = run(cmd, capture_output=True, check=True).stdout
+    except CalledProcessError as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
+
+
+def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
+    """
+    Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+    """
+    if torch.is_tensor(array):
+        if array.shape[axis] > length:
+            array = array.index_select(
+                dim=axis, index=torch.arange(length, device=array.device)
+            )
+
+        if array.shape[axis] < length:
+            pad_widths = [(0, 0)] * array.ndim
+            pad_widths[axis] = (0, length - array.shape[axis])
+            array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
+    else:
+        if array.shape[axis] > length:
+            array = array.take(indices=range(length), axis=axis)
+
+        if array.shape[axis] < length:
+            pad_widths = [(0, 0)] * array.ndim
+            pad_widths[axis] = (0, length - array.shape[axis])
+            array = np.pad(array, pad_widths)
+
+    return array
+
+def trim(array, length: int = N_SAMPLES, *, axis: int = -1):
+    """
+    Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+    """
+    if torch.is_tensor(array):
+        if array.shape[axis] > length:
+            array = array.index_select(
+                dim=axis, index=torch.arange(length, device=array.device)
+            )
+    else:
+        if array.shape[axis] > length:
+            array = array.take(indices=range(length), axis=axis)
+    return array
+
+
+@lru_cache(maxsize=None)
+def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
+    """
+    load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
+    Allows decoupling librosa dependency; saved using:
+        np.savez_compressed(
+            "mel_filters.npz",
+            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
+        )
+    """
+    assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
+    with np.load(
+        os.path.join(os.path.dirname(__file__), "mel_filters.npz") # todo
+        # os.path.join("assets", "mel_filters.npz")
+    ) as f:
+        return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
+
+
+def log_mel_spectrogram(
+    audio: Union[str, np.ndarray, torch.Tensor],
+    n_mels: int = N_MELS,
+    padding: int = 0,
+    device: Optional[Union[str, torch.device]] = None,
+):
+    """
+    Compute the log-Mel spectrogram of
+    Parameters
+    ----------
+    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
+        The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
+    n_mels: int
+        The number of Mel-frequency filters, only 80 is supported
+    padding: int
+        Number of zero samples to pad to the right
+    device: Optional[Union[str, torch.device]]
+        If given, the audio tensor is moved to this device before STFT
+    Returns
+    -------
+    torch.Tensor, shape = (80, n_frames)
+        A Tensor that contains the Mel spectrogram
+    """
+    if not torch.is_tensor(audio):
+        if isinstance(audio, str):
+            audio = load_audio(audio)
+        audio = torch.from_numpy(audio)
+
+    if device is not None:
+        audio = audio.to(device)
+    if padding > 0:
+        audio = F.pad(audio, (0, padding))
+    window = torch.hann_window(N_FFT).to(audio.device)
+    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
+    magnitudes = stft[..., :-1].abs() ** 2
+
+    filters = mel_filters(audio.device, n_mels)
+    mel_spec = filters @ magnitudes
+
+    log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+    log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+    log_spec = (log_spec + 4.0) / 4.0
+    return log_spec
+
+
+@dataclass
+class ModelDimensions:
+    n_mels: int
+    n_audio_ctx: int
+    n_audio_state: int
+    n_audio_head: int
+    n_audio_layer: int
+    n_vocab: int
+    n_text_ctx: int
+    n_text_state: int
+    n_text_head: int
+    n_text_layer: int
+
+
+class LayerNorm(nn.LayerNorm):
+    def forward(self, x: Tensor) -> Tensor:
+        # return super().forward(x.float()).type(x.dtype)
+        return super().forward(x).type(x.dtype)
+
+
+
+
+class Linear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        return F.linear(
+            x,
+            self.weight.to(x.dtype),
+            None if self.bias is None else self.bias.to(x.dtype),
+        )
+
+
+class Conv1d(nn.Conv1d):
+    def _conv_forward(
+        self, x: Tensor, weight: Tensor, bias: Optional[Tensor]
+    ) -> Tensor:
+        return super()._conv_forward(
+            x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
+        )
+
+
+def sinusoids(length, channels, max_timescale=10000):
+    """Returns sinusoids for positional embedding"""
+    assert channels % 2 == 0
+    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
+    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, n_state: int, n_head: int):
+        super().__init__()
+        self.n_head = n_head
+        self.query = Linear(n_state, n_state)
+        self.key = Linear(n_state, n_state, bias=False)
+        self.value = Linear(n_state, n_state)
+        self.out = Linear(n_state, n_state)
+
+    def forward(
+        self,
+        x: Tensor,
+        xa: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        kv_cache: Optional[dict] = None,
+    ):
+        q = self.query(x)
+
+        if kv_cache is None or xa is None or self.key not in kv_cache:
+            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
+            # otherwise, perform key/value projections for self- or cross-attention as usual.
+            k = self.key(x if xa is None else xa)
+            v = self.value(x if xa is None else xa)
+        else:
+            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
+            k = kv_cache[self.key]
+            v = kv_cache[self.value]
+
+        wv, qk = self.qkv_attention(q, k, v, mask)
+        return self.out(wv), qk
+
+    def qkv_attention(
+        self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None
+    ):
+        n_batch, n_ctx, n_state = q.shape
+        scale = (n_state // self.n_head) ** -0.25
+        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
+        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
+        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+
+        qk = q @ k
+        if mask is not None:
+            qk += mask
+
+        w = F.softmax(qk, dim=-1).to(q.dtype)
+        return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach()
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
+        super().__init__()
+
+        self.attn = MultiHeadAttention(n_state, n_head)
+        self.attn_ln = LayerNorm(n_state)
+
+        self.cross_attn = (
+            MultiHeadAttention(n_state, n_head) if cross_attention else None
+        )
+        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
+
+        n_mlp = n_state * 4
+        self.mlp = nn.Sequential(
+            Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)
+        )
+        self.mlp_ln = LayerNorm(n_state)
+
+    def forward(
+        self,
+        x: Tensor,
+        xa: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        kv_cache: Optional[dict] = None,
+    ):
+        x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0]
+        if self.cross_attn:
+            x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0]
+        x = x + self.mlp(self.mlp_ln(x))
+        return x
+
+
+class AudioEncoder(nn.Module):
+    def __init__(
+            self,
+            n_mels: int,
+            n_ctx: int,
+            n_state: int,
+            n_head: int,
+            n_layer: int,
+            output_dim: int = 512,
+            avg_pool: bool = True,
+            add_audio_bos_eos_token: bool = True,
+            **kwargs
+    ):
+        super().__init__()
+        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
+        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
+        self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
+
+        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+            [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
+        )
+        self.ln_post = LayerNorm(n_state)
+
+        if avg_pool:
+            self.avg_pooler = nn.AvgPool1d(2, stride=2)
+        else:
+            self.avg_pooler = None
+        self.proj = nn.Linear(n_state, output_dim)
+        if add_audio_bos_eos_token:
+            self.audio_bos_eos_token = nn.Embedding(2, output_dim)
+        else:
+            self.audio_bos_eos_token = None
+        self.output_dim = output_dim
+        self.n_head = n_head
+
+    def forward(self, x: Tensor, padding_mask: Tensor=None, audio_lengths: Tensor=None):
+        """
+        x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
+            the mel spectrogram of the audio
+        """
+        x = x.to(dtype=self.conv1.weight.dtype,
+                 device=self.conv1.weight.device)
+        if audio_lengths is not None:
+            input_mel_len = audio_lengths[:,0] * 2
+            max_mel_len_in_batch = input_mel_len.max()
+            x = x[:, :, :max_mel_len_in_batch]
+        x = F.gelu(self.conv1(x))
+        x = F.gelu(self.conv2(x))
+        x = x.permute(0, 2, 1)  # B, L, D
+        bsz = x.size(0)
+        src_len = x.size(1)
+
+
+        self.input_positional_embedding = self.positional_embedding[:src_len]
+        assert x.shape[1:] == self.input_positional_embedding.shape, f"incorrect audio shape: {x.shape[1:], self.input_positional_embedding.shape}"
+        x = (x + self.input_positional_embedding).to(x.dtype)
+        if padding_mask is not None:
+            padding_mask = padding_mask.to(dtype=self.conv1.weight.dtype,
+                     device=self.conv1.weight.device)
+            batch_src_len = padding_mask.size(1)
+            x = x[:, :batch_src_len, :]
+            padding_mask = padding_mask.view(
+                bsz, -1, batch_src_len
+            )
+            padding_mask_ = padding_mask.all(1)
+            x[padding_mask_] = 0
+            key_padding_mask = padding_mask_.view(bsz, 1, 1, batch_src_len). \
+                expand(-1, self.n_head, -1, -1).reshape(bsz, self.n_head, 1, batch_src_len)
+            new_padding_mask = torch.zeros_like(key_padding_mask, dtype=x.dtype)
+            padding_mask = new_padding_mask.masked_fill(key_padding_mask, float("-inf"))
+
+        for block in self.blocks:
+            x = block(x, mask=padding_mask)
+
+
+        if self.avg_pooler:
+            x = x.permute(0, 2, 1)
+            x = self.avg_pooler(x)
+            x = x.permute(0, 2, 1)
+
+
+        x = self.ln_post(x)
+        x = self.proj(x)
+
+        if self.audio_bos_eos_token is not None:
+            bos = self.audio_bos_eos_token.weight[0][None, :]
+            eos = self.audio_bos_eos_token.weight[1][None, :]
+        else:
+            bos, eos = None, None
+        return x, bos, eos
+
+    def encode(self, input_audios: Tensor, input_audio_lengths: Tensor, audio_span_tokens: List):
+        real_input_audio_lens = input_audio_lengths[:, 0].tolist()
+        max_len_in_batch = max(real_input_audio_lens)
+        padding_mask = torch.ones([input_audios.size(0), max_len_in_batch]).to(dtype=self.conv1.weight.dtype,
+                                                                               device=self.conv1.weight.device)
+        for index in range(len(input_audios)):
+            padding_mask[index, :input_audio_lengths[index][0].item()] = 0
+        x, bos, eos = self(input_audios, padding_mask,input_audio_lengths)
+        output_audios = []
+        for i in range(len(audio_span_tokens)):
+            audio_span = audio_span_tokens[i]
+            audio = x[i][:audio_span-2]
+            if bos is not None:
+                audio = torch.concat([bos, audio, eos])
+            assert len(audio) == audio_span
+            output_audios.append(audio)
+        return output_audios