f1

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: Chtrbx
+emoji: ⚡
+colorFrom: red
+colorTo: purple
+sdk: gradio
+sdk_version: 5.42.0
+app_file: app.py
+pinned: false
+license: cc-by-nc-4.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import random
+import numpy as np
+import torch
+import gradio as gr
+from chatterbox.tts import ChatterboxTTS
+import json
+from pathlib import Path
+from torch import nn, Tensor
+
+import threading
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+t3_model_path="https://huggingface.co/Karayakar/PRVCHTBX/resolve/main/model.safetensors"
+tokenizer_path="https://huggingface.co/Karayakar/PRVCHTBX/resolve/main/tokenizer.json"
+model=None
+import functools
+
+_MODEL_SINGLETON = None
+_MODEL_LOCK = threading.Lock()
+
+
+def get_model_singleton():
+    global _MODEL_SINGLETON
+    if _MODEL_SINGLETON is None:
+        with _MODEL_LOCK:
+            if _MODEL_SINGLETON is None:   # double-checked locking
+                _MODEL_SINGLETON = load_model_init()
+    return _MODEL_SINGLETON
+
+def set_seed(seed: int):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    random.seed(seed)
+    np.random.seed(seed)
+
+def set_inference_flags(tts, device="cuda", dtype=torch.bfloat16):
+    # t3 (LLM tarafı)
+    if hasattr(tts, "t3") and isinstance(tts.t3, nn.Module):
+        tts.t3.eval()
+        try:
+            tts.t3.to(device=device, dtype=dtype)
+        except Exception:
+            pass
+        # varsa HF backbone'u derle
+        if hasattr(tts.t3, "tfmr"):
+            try:
+                tts.t3.tfmr = torch.compile(tts.t3.tfmr, mode="reduce-overhead")
+            except Exception as e:
+                print("compile skipped:", e)
+
+    # s3gen (speech generator / tokenizer çevresi)
+    if hasattr(tts, "s3gen") and isinstance(tts.s3gen, nn.Module):
+        tts.s3gen.eval()
+        try:
+            tts.s3gen.to(device=device, dtype=dtype)
+        except Exception:
+            pass
+
+    # voice encoder
+    if hasattr(tts, "ve") and isinstance(tts.ve, nn.Module):
+        tts.ve.eval()
+        try:
+            tts.ve.to(device=device, dtype=dtype)
+        except Exception:
+            pass
+
+def load_model():
+    return get_model_singleton()
+    
+def load_model_init():
+    print("LOAD MODEL CALLED")
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    t3_model_path="https://huggingface.co/Karayakar/PRVCHTBX/resolve/main/model.safetensors"
+    tokenizer_path="https://huggingface.co/Karayakar/PRVCHTBX/resolve/main/tokenizer.json"
+    
+    with open("/model_path.json", "r") as f:
+       model_path = json.load(f)
+        
+    
+    if not t3_model_path or not Path(t3_model_path).exists():
+        raise gr.Error("Please select a valid T3 model file")
+    
+    voice_encoder_path = model_path["voice_encoder_path"]
+    s3gen_path = model_path["s3gen_path"]
+    
+    if not tokenizer_path or not Path(tokenizer_path).exists():
+        tokenizer_path = model_path["tokenizer_path"]
+    
+    conds_path = Path(model_path["conds_path"])
+
+    #if model is None:
+        #model = ChatterboxTTS.from_pretrained(DEVICE)
+    model = ChatterboxTTS.from_specified(
+            voice_encoder_path=voice_encoder_path,
+            t3_path=t3_model_path,
+            s3gen_path=s3gen_path,
+            tokenizer_path=tokenizer_path,
+            conds_path=conds_path,
+            device=device)
+    #model = ChatterboxTTS.from_pretrained(DEVICE)
+    
+    # --- HIZ AYARLARI: tam burada yap ---
+    # hız bayrakları (3090)
+    torch.set_float32_matmul_precision("high")
+    from torch.backends.cuda import sdp_kernel
+    sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False)
+
+    # weights float32 doesn't work --> set_inference_flags(model, device=device, dtype=torch.bfloat16)  # <- burada
+    torch.backends.cudnn.benchmark = True
+    
+    
+    return model
+
+
+    
+    
+def generate( text, audio_prompt_path, exaggeration, temperature, seed_num, cfgw, min_p, top_p, repetition_penalty):
+    #t3_model_path="G:\\OPENAI\\chatterbox\\checkpoint_159960\\model.safetensors"
+    #tokenizer_path="G:\\OPENAI\\chatterbox\\checkpoint_159960\\tokenizer.json"
+    #device = "cuda" if torch.cuda.is_available() else "cpu"
+    #with open("G:\\OPENAI\\chatterbox\\checkpoint_159960\\model_path.json", "r") as f:
+    #    model_path = json.load(f)
+    #    
+    #
+    #if not t3_model_path or not Path(t3_model_path).exists():
+    #    raise gr.Error("Please select a valid T3 model file")
+    #
+    #voice_encoder_path = model_path["voice_encoder_path"]
+    #s3gen_path = model_path["s3gen_path"]
+    #
+    #if not tokenizer_path or not Path(tokenizer_path).exists():
+    #    tokenizer_path = model_path["tokenizer_path"]
+    #
+    #conds_path = Path(model_path["conds_path"])
+    #
+    #if model is None:
+    #    #model = ChatterboxTTS.from_pretrained(DEVICE)
+    #    model = ChatterboxTTS.from_specified(
+    #        voice_encoder_path=voice_encoder_path,
+    #        t3_path=t3_model_path,
+    #        s3gen_path=s3gen_path,
+    #        tokenizer_path=tokenizer_path,
+    #        conds_path=conds_path,
+    #        device=device)
+    
+    #if model is None:
+    model=get_model_singleton()
+        
+    if seed_num != 0:
+        set_seed(int(seed_num))
+
+    #wav = model.generate(
+    #    text,
+    #    audio_prompt_path=audio_prompt_path,
+    #    exaggeration=exaggeration,
+    #    temperature=temperature,
+    #    cfg_weight=cfgw,
+    #    min_p=min_p,
+    #    top_p=top_p,
+    #    repetition_penalty=repetition_penalty,
+    #)
+    
+    wav= model.generate(
+      
+
+            text,
+            repetition_penalty=repetition_penalty,
+            min_p=min_p,
+            top_p=top_p,
+            audio_prompt_path=audio_prompt_path,
+            exaggeration=exaggeration,
+            cfg_weight=cfgw,
+            temperature=temperature,
+
+
+        )
+    return (model.sr, wav.squeeze(0).numpy())
+
+
+with gr.Blocks() as demo:
+    #model_state = gr.State(None)  # Loaded once per session/user
+
+    with gr.Row():
+        with gr.Column():
+            text = gr.Textbox(
+                value="Yapzek.ai, çok yakında ses klonlama özelliğiyle sınırları bir adım daha öteye taşıyor! Kendi sesinizi birkaç dakikalık örnekle klonlayabilecek, dilediğiniz metni kendi sesinizle, üstelik duygulu ve doğal bir şekilde seslendirebileceksiniz.",
+                label="Text to synthesize (max chars 300)",
+                max_lines=5
+            )
+            ref_wav = gr.Audio(sources=["upload", "microphone"], type="filepath", label="Reference Audio File", value=None)
+            exaggeration = gr.Slider(0.25, 2, step=.05, label="Exaggeration (Neutral = 0.5, extreme values can be unstable)", value=.5)
+            cfg_weight = gr.Slider(0.0, 1, step=.05, label="CFG/Pace", value=0.5)
+
+            with gr.Accordion("More options", open=False):
+                seed_num = gr.Number(value=0, label="Random seed (0 for random)")
+                temp = gr.Slider(0.05, 5, step=.05, label="temperature", value=.8)
+                min_p = gr.Slider(0.00, 1.00, step=0.01, label="min_p || Newer Sampler. Recommend 0.02 > 0.1. Handles Higher Temperatures better. 0.00 Disables", value=0.05)
+                top_p = gr.Slider(0.00, 1.00, step=0.01, label="top_p || Original Sampler. 1.0 Disables(recommended). Original 0.8", value=1.00)
+                repetition_penalty = gr.Slider(1.00, 2.00, step=0.1, label="repetition_penalty", value=1.2)
+
+            run_btn = gr.Button("Generate", variant="primary")
+
+        with gr.Column():
+            audio_output = gr.Audio(label="Output Audio")
+
+    demo.load(fn=load_model, inputs=[])#, outputs=model_state)
+
+    run_btn.click(
+        fn=generate,
+        inputs=[
+            #model_state,
+            text,
+            ref_wav,
+            exaggeration,
+            temp,
+            seed_num,
+            cfg_weight,
+            min_p,
+            top_p,
+            repetition_penalty,
+        ],
+        outputs=audio_output,
+    )
+
+if __name__ == "__main__":
+    demo.queue(
+        max_size=50,
+        default_concurrency_limit=1,
+    ).launch(share=True)

chatterbox/__init__.py

@@ -0,0 +1,10 @@
+try:
+    from importlib.metadata import version
+except ImportError:
+    from importlib_metadata import version  # For Python <3.8
+
+__version__ = version("chatterbox-tts")
+
+
+from .tts import ChatterboxTTS
+from .vc import ChatterboxVC

chatterbox/models/model_v2.py

@@ -0,0 +1,405 @@
+# Copyright (c)  (Mddct: Dinghao Zhou)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import Optional, Tuple
+
+import torch
+from einops import rearrange
+
+from s3tokenizer.model import Conv1d, LayerNorm, Linear, MultiHeadAttention
+from s3tokenizer.utils import make_non_pad_mask, mask_to_bias, onnx2torch
+
+
+@dataclass
+class ModelConfig:
+    n_mels: int = 128
+    n_audio_ctx: int = 1500
+    n_audio_state: int = 1280
+    n_audio_head: int = 20
+    n_audio_layer: int = 6
+    n_codebook_size: int = 3**8
+
+    use_sdpa: bool = False
+
+
+def precompute_freqs_cis(dim: int,
+                         end: int,
+                         theta: float = 10000.0,
+                         scaling=None):
+    freqs = 1.0 / (theta**(torch.arange(0, dim, 2)[:(dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    if scaling is not None:
+        t = t * scaling
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+
+    return torch.cat((freqs_cis, freqs_cis), dim=-1)
+
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    real = torch.view_as_real(freqs_cis)
+    cos, sin = real[:, :, 0], real[:, :, 1]
+    cos = cos.unsqueeze(0).unsqueeze(2)
+    sin = sin.unsqueeze(0).unsqueeze(2)
+
+    D = xq.shape[-1]
+    half_l, half_r = xq[:, :, :, :D // 2], xq[:, :, :, D // 2:]
+    xq_r = torch.cat((-half_r, half_l), dim=-1)
+
+    D = xk.shape[-1]
+
+    half_l, half_r = xk[:, :, :, :D // 2], xk[:, :, :, D // 2:]
+    xk_r = torch.cat((-half_r, half_l), dim=-1)
+
+    return xq * cos + xq_r * sin, xk * cos + xk_r * sin
+
+
+def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+    assert freqs_cis.shape == (x.shape[1], x.shape[-1])
+    shape = [
+        d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)
+    ]
+    return freqs_cis.view(*shape)
+
+
+class FSQCodebook(torch.nn.Module):
+
+    def __init__(self, dim: int, level: int = 3):
+        super().__init__()
+        self.project_down = torch.nn.Linear(dim, 8)
+        self.level = level
+        self.embed = None
+
+    @torch.inference_mode()
+    def preprocess(self, x: torch.Tensor) -> torch.Tensor:
+        x = rearrange(x, "... d -> (...) d")
+        return x
+
+    @torch.inference_mode()
+    def encode(self, x: torch.Tensor) -> torch.Tensor:
+        x_shape = x.shape
+        # pre-process
+        x = self.preprocess(x)
+        # quantize
+        h = self.project_down(x).float()
+        h = h.tanh()
+        h = h * 0.9990000128746033
+        h = h.round() + 1
+        # h = ((self.level - 1) * h).round()  # range [-k, k]
+        powers = torch.pow(
+            self.level,
+            torch.arange(2**self.level, device=x.device, dtype=h.dtype))
+        mu = torch.sum(h * powers.unsqueeze(0), dim=-1)
+        ind = mu.reshape(x_shape[0], x_shape[1]).int()
+        return ind
+
+    @torch.inference_mode()
+    def decode(self, embed_ind: torch.Tensor) -> torch.Tensor:
+        raise NotImplementedError(
+            'There is no official up project component provided')
+
+
+class FSQVectorQuantization(torch.nn.Module):
+    """Vector quantization implementation (inference-only).
+    Args:
+        dim (int): Dimension
+        codebook_size (int): Codebook size
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        codebook_size: int,
+    ):
+        super().__init__()
+        assert 3**8 == codebook_size
+        self._codebook = FSQCodebook(dim=dim, level=3)
+        self.codebook_size = codebook_size
+
+    @property
+    def codebook(self):
+        return self._codebook.embed
+
+    @torch.inference_mode()
+    def encode(self, x: torch.Tensor) -> torch.Tensor:
+        return self._codebook.encode(x)
+
+    @torch.inference_mode()
+    def decode(self, embed_ind: torch.Tensor) -> torch.Tensor:
+        quantize = self._codebook.decode(embed_ind)
+        quantize = rearrange(quantize, "b n d -> b d n")
+        return quantize
+
+
+class FSMNMultiHeadAttention(MultiHeadAttention):
+
+    def __init__(
+        self,
+        n_state: int,
+        n_head: int,
+        kernel_size: int = 31,
+        use_sdpa: bool = False,
+    ):
+        super().__init__(n_state, n_head)
+
+        self.fsmn_block = torch.nn.Conv1d(n_state,
+                                          n_state,
+                                          kernel_size,
+                                          stride=1,
+                                          padding=0,
+                                          groups=n_state,
+                                          bias=False)
+        self.left_padding = (kernel_size - 1) // 2
+        self.right_padding = kernel_size - 1 - self.left_padding
+        self.pad_fn = torch.nn.ConstantPad1d(
+            (self.left_padding, self.right_padding), 0.0)
+
+        self.use_sdpa = use_sdpa
+
+    def forward_fsmn(self,
+                     inputs: torch.Tensor,
+                     mask: Optional[torch.Tensor] = None):
+        b, t, _, _ = inputs.size()
+        inputs = inputs.view(b, t, -1)
+        if mask is not None and mask.size(2) > 0:  # time2 > 0
+            inputs = inputs * mask
+        x = inputs.transpose(1, 2)
+        x = self.pad_fn(x)
+        x = self.fsmn_block(x)
+        x = x.transpose(1, 2)
+        x += inputs
+        return x * mask
+
+    def qkv_attention(self,
+                      q: torch.Tensor,
+                      k: torch.Tensor,
+                      v: torch.Tensor,
+                      mask: Optional[torch.Tensor] = None,
+                      mask_pad: Optional[torch.Tensor] = None,
+                      freqs_cis: Optional[torch.Tensor] = None):
+        _, _, D = q.shape
+        scale = (D // self.n_head)**-0.25
+        q = q.view(*q.shape[:2], self.n_head, -1)
+        k = k.view(*k.shape[:2], self.n_head, -1)
+        v = v.view(*v.shape[:2], self.n_head, -1)
+
+        if freqs_cis is not None:
+            q, k = apply_rotary_emb(q, k, freqs_cis=freqs_cis)
+
+        fsm_memory = self.forward_fsmn(v, mask_pad)
+
+        q = q.permute(0, 2, 1, 3) * scale
+        v = v.permute(0, 2, 1, 3)
+
+        if not self.use_sdpa:
+            k = k.permute(0, 2, 3, 1) * scale
+            qk = q @ k  # (B, n_head, T, T)
+            if mask is not None:
+                qk = qk + mask
+            qk = qk.float()
+            w = torch.nn.functional.softmax(qk, dim=-1).to(q.dtype)
+            return (w @ v).permute(
+                0, 2, 1, 3).flatten(start_dim=2), qk.detach(), fsm_memory
+        else:
+            k = k.permute(0, 2, 1, 3) * scale
+            assert mask is not None
+            output = torch.nn.functional.scaled_dot_product_attention(
+                q,
+                k,
+                v,
+                attn_mask=mask,
+                dropout_p=0.,
+                scale=1.,
+            )
+            output = (output.transpose(1,
+                                       2).contiguous().view(q.size(0), -1, D)
+                      )  # (batch, time1, d_model)
+            return output, None, fsm_memory
+
+    def forward(self,
+                x: torch.Tensor,
+                mask: Optional[torch.Tensor] = None,
+                mask_pad: Optional[torch.Tensor] = None,
+                freqs_cis: Optional[torch.Tensor] = None):
+
+        q = self.query(x)
+        k = self.key(x)
+        v = self.value(x)
+
+        wv, qk, fsm_memory = self.qkv_attention(q, k, v, mask, mask_pad,
+                                                freqs_cis)
+        return self.out(wv) + fsm_memory, qk
+
+
+class ResidualAttentionBlock(torch.nn.Module):
+
+    def __init__(
+        self,
+        n_state: int,
+        n_head: int,
+        kernel_size: int = 31,
+        use_sdpa: bool = False,
+    ):
+        super().__init__()
+
+        self.attn = FSMNMultiHeadAttention(n_state,
+                                           n_head,
+                                           kernel_size,
+                                           use_sdpa=use_sdpa)
+        self.attn_ln = LayerNorm(n_state, eps=1e-6)
+
+        n_mlp = n_state * 4
+
+        self.mlp = torch.nn.Sequential(Linear(n_state, n_mlp), torch.nn.GELU(),
+                                       Linear(n_mlp, n_state))
+        self.mlp_ln = LayerNorm(n_state)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+        mask_pad: Optional[torch.Tensor] = None,
+        freqs_cis: Optional[torch.Tensor] = None,
+    ):
+        x = x + self.attn(
+            self.attn_ln(x), mask=mask, mask_pad=mask_pad,
+            freqs_cis=freqs_cis)[0]
+
+        x = x + self.mlp(self.mlp_ln(x))
+        return x
+
+
+class AudioEncoderV2(torch.nn.Module):
+
+    def __init__(
+        self,
+        n_mels: int,
+        n_state: int,
+        n_head: int,
+        n_layer: int,
+        stride: int,
+        use_sdpa: bool,
+    ):
+        super().__init__()
+        self.stride = stride
+
+        self.conv1 = Conv1d(n_mels,
+                            n_state,
+                            kernel_size=3,
+                            stride=stride,
+                            padding=1)
+        self.conv2 = Conv1d(n_state,
+                            n_state,
+                            kernel_size=3,
+                            stride=2,
+                            padding=1)
+        self.freqs_cis = precompute_freqs_cis(64, 1024 * 2)
+        self.blocks = torch.nn.ModuleList([
+            ResidualAttentionBlock(n_state, n_head, use_sdpa=use_sdpa)
+            for _ in range(n_layer)
+        ])
+
+    def forward(self, x: torch.Tensor,
+                x_len: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        x : torch.Tensor, shape = (batch_size, n_mels, T)
+            the mel spectrogram of the audio
+        x_len: torch.Tensor, shape = (batch_size,)
+            length of each audio in x
+        """
+        mask = make_non_pad_mask(x_len).unsqueeze(1)
+        x = torch.nn.functional.gelu(self.conv1(x * mask))
+        x_len = (x_len + 2 - 1 * (3 - 1) - 1) // self.stride + 1
+        mask = make_non_pad_mask(x_len).unsqueeze(1)
+        x = torch.nn.functional.gelu(self.conv2(x * mask))
+        x_len = (x_len + 2 - 1 * (3 - 1) - 1) // 2 + 1
+        mask = make_non_pad_mask(x_len).unsqueeze(1)
+        x = x.permute(0, 2, 1)  # (B, T // 2, n_state)
+        freqs_cis = self.freqs_cis.to(x.device)
+        mask_pad = mask.transpose(1, 2)
+        mask = mask_to_bias(mask, x.dtype)
+
+        tmp = torch.view_as_real(freqs_cis)
+        cos, sin = tmp[:, :, 0], tmp[:, :, 1]
+
+        cos = torch.cat((cos, cos), dim=-1)
+        sin = torch.cat((sin, sin), dim=-1)
+        cos = cos.unsqueeze(0).unsqueeze(2)
+        sin = sin.unsqueeze(0).unsqueeze(2)
+
+        for block in self.blocks:
+            x = block(x, mask.unsqueeze(1), mask_pad, freqs_cis[:x.size(1)])
+
+        return x, x_len
+
+
+class S3TokenizerV2(torch.nn.Module):
+    """S3 tokenizer v2 implementation (inference-only).
+    Args:
+        config (ModelConfig): Config
+    """
+
+    def __init__(self, name: str, config: ModelConfig = ModelConfig()):
+        super().__init__()
+        if 'v1' not in name:
+            assert 'v2' in name
+            # TODO(Mddct): make it configureable
+            config.n_codebook_size = 3**8
+        self.config = config
+        self.encoder = AudioEncoderV2(
+            self.config.n_mels,
+            self.config.n_audio_state,
+            self.config.n_audio_head,
+            self.config.n_audio_layer,
+            2,
+            self.config.use_sdpa,
+        )
+        self.quantizer = FSQVectorQuantization(
+            self.config.n_audio_state,
+            self.config.n_codebook_size,
+        )
+
+    def forward(self, mel: torch.Tensor,
+                mel_len: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        return self.quantize(mel, mel_len)
+
+    @torch.inference_mode()
+    def quantize(self, mel: torch.Tensor,
+                 mel_len: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        hidden, code_len = self.encoder(mel, mel_len)
+        code = self.quantizer.encode(hidden)
+        return code, code_len
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def init_from_onnx(self, onnx_path: str):
+        ckpt = onnx2torch(onnx_path, None, False)
+        self.load_state_dict(ckpt, strict=True)
+
+    def init_from_pt(self, ckpt_path: str):
+        ckpt = torch.load(ckpt_path, map_location="cpu", mmap=True)
+        self.load_state_dict(ckpt, strict=True)
+
+    def freeze(self):
+        for _, param in self.named_parameters():
+            param.requires_grad = False

chatterbox/models/s3gen/__init__.py

@@ -0,0 +1,2 @@
+from .s3gen import S3Token2Wav as S3Gen
+from .const import S3GEN_SR

chatterbox/models/s3gen/configs.py

@@ -0,0 +1,10 @@
+from ..utils import AttrDict
+
+CFM_PARAMS = AttrDict({
+    "sigma_min": 1e-06,
+    "solver": "euler",
+    "t_scheduler": "cosine",
+    "training_cfg_rate": 0.2,
+    "inference_cfg_rate": 0.7,
+    "reg_loss_type": "l1"
+})

chatterbox/models/s3gen/const.py

@@ -0,0 +1 @@
+S3GEN_SR = 24000

chatterbox/models/s3gen/decoder.py

@@ -0,0 +1,317 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import pack, rearrange, repeat
+
+from .utils.mask import add_optional_chunk_mask
+from .matcha.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, \
+    TimestepEmbedding, Upsample1D
+from .matcha.transformer import BasicTransformerBlock
+
+
+def mask_to_bias(mask: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
+    assert mask.dtype == torch.bool
+    assert dtype in [torch.float32, torch.bfloat16, torch.float16]
+    mask = mask.to(dtype)
+    # attention mask bias
+    # NOTE(Mddct): torch.finfo jit issues
+    #     chunk_masks = (1.0 - chunk_masks) * torch.finfo(dtype).min
+    mask = (1.0 - mask) * -1.0e+10
+    return mask
+
+
+
+class Transpose(torch.nn.Module):
+    def __init__(self, dim0: int, dim1: int):
+        super().__init__()
+        self.dim0 = dim0
+        self.dim1 = dim1
+
+    def forward(self, x: torch.Tensor):
+        x = torch.transpose(x, self.dim0, self.dim1)
+        return x
+
+
+class CausalBlock1D(Block1D):
+    def __init__(self, dim: int, dim_out: int):
+        super(CausalBlock1D, self).__init__(dim, dim_out)
+        self.block = torch.nn.Sequential(
+            CausalConv1d(dim, dim_out, 3),
+            Transpose(1, 2),
+            nn.LayerNorm(dim_out),
+            Transpose(1, 2),
+            nn.Mish(),
+        )
+
+    def forward(self, x: torch.Tensor, mask: torch.Tensor):
+        output = self.block(x * mask)
+        return output * mask
+
+
+class CausalResnetBlock1D(ResnetBlock1D):
+    def __init__(self, dim: int, dim_out: int, time_emb_dim: int, groups: int = 8):
+        super(CausalResnetBlock1D, self).__init__(dim, dim_out, time_emb_dim, groups)
+        self.block1 = CausalBlock1D(dim, dim_out)
+        self.block2 = CausalBlock1D(dim_out, dim_out)
+
+
+class CausalConv1d(torch.nn.Conv1d):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        dilation: int = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: str = 'zeros',
+        device=None,
+        dtype=None
+    ) -> None:
+        super(CausalConv1d, self).__init__(in_channels, out_channels,
+                                           kernel_size, stride,
+                                           padding=0, dilation=dilation,
+                                           groups=groups, bias=bias,
+                                           padding_mode=padding_mode,
+                                           device=device, dtype=dtype)
+        assert stride == 1
+        self.causal_padding = (kernel_size - 1, 0)
+
+    def forward(self, x: torch.Tensor):
+        x = F.pad(x, self.causal_padding)
+        x = super(CausalConv1d, self).forward(x)
+        return x
+
+
+class ConditionalDecoder(nn.Module):
+    def __init__(
+        self,
+        in_channels=320,
+        out_channels=80,
+        causal=True,
+        channels=[256],
+        dropout=0.0,
+        attention_head_dim=64,
+        n_blocks=4,
+        num_mid_blocks=12,
+        num_heads=8,
+        act_fn="gelu",
+    ):
+        """
+        This decoder requires an input with the same shape of the target. So, if your text content
+        is shorter or longer than the outputs, please re-sampling it before feeding to the decoder.
+        """
+        super().__init__()
+        channels = tuple(channels)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.causal = causal
+        self.time_embeddings = SinusoidalPosEmb(in_channels)
+        time_embed_dim = channels[0] * 4
+        self.time_mlp = TimestepEmbedding(
+            in_channels=in_channels,
+            time_embed_dim=time_embed_dim,
+            act_fn="silu",
+        )
+        self.down_blocks = nn.ModuleList([])
+        self.mid_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        # NOTE jrm: `static_chunk_size` is missing?
+        self.static_chunk_size = 0
+
+        output_channel = in_channels
+        for i in range(len(channels)):  # pylint: disable=consider-using-enumerate
+            input_channel = output_channel
+            output_channel = channels[i]
+            is_last = i == len(channels) - 1
+            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) if self.causal else \
+                ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+            transformer_blocks = nn.ModuleList(
+                [
+                    BasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            downsample = (
+                Downsample1D(output_channel) if not is_last else
+                CausalConv1d(output_channel, output_channel, 3) if self.causal else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+            )
+            self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
+
+        for _ in range(num_mid_blocks):
+            input_channel = channels[-1]
+            out_channels = channels[-1]
+            resnet = CausalResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim) if self.causal else \
+                ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+
+            transformer_blocks = nn.ModuleList(
+                [
+                    BasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+
+            self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
+
+        channels = channels[::-1] + (channels[0],)
+        for i in range(len(channels) - 1):
+            input_channel = channels[i] * 2
+            output_channel = channels[i + 1]
+            is_last = i == len(channels) - 2
+            resnet = CausalResnetBlock1D(
+                dim=input_channel,
+                dim_out=output_channel,
+                time_emb_dim=time_embed_dim,
+            ) if self.causal else ResnetBlock1D(
+                dim=input_channel,
+                dim_out=output_channel,
+                time_emb_dim=time_embed_dim,
+            )
+            transformer_blocks = nn.ModuleList(
+                [
+                    BasicTransformerBlock(
+                        dim=output_channel,
+                        num_attention_heads=num_heads,
+                        attention_head_dim=attention_head_dim,
+                        dropout=dropout,
+                        activation_fn=act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            upsample = (
+                Upsample1D(output_channel, use_conv_transpose=True)
+                if not is_last
+                else CausalConv1d(output_channel, output_channel, 3) if self.causal else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+            )
+            self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
+        self.final_block = CausalBlock1D(channels[-1], channels[-1]) if self.causal else Block1D(channels[-1], channels[-1])
+        self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d):
+                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.GroupNorm):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.Linear):
+                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x, mask, mu, t, spks=None, cond=None):
+        """Forward pass of the UNet1DConditional model.
+
+        Args:
+            x (torch.Tensor): shape (batch_size, in_channels, time)
+            mask (_type_): shape (batch_size, 1, time)
+            t (_type_): shape (batch_size)
+            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
+            cond (_type_, optional): placeholder for future use. Defaults to None.
+
+        Raises:
+            ValueError: _description_
+            ValueError: _description_
+
+        Returns:
+            _type_: _description_
+        """
+
+        t = self.time_embeddings(t).to(t.dtype)
+        t = self.time_mlp(t)
+
+        x = pack([x, mu], "b * t")[0]
+
+        if spks is not None:
+            spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
+            x = pack([x, spks], "b * t")[0]
+        if cond is not None:
+            x = pack([x, cond], "b * t")[0]
+
+        hiddens = []
+        masks = [mask]
+        for resnet, transformer_blocks, downsample in self.down_blocks:
+            mask_down = masks[-1]
+            x = resnet(x, mask_down, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            # attn_mask = torch.matmul(mask_down.transpose(1, 2).contiguous(), mask_down)
+            attn_mask = add_optional_chunk_mask(x, mask_down.bool(), False, False, 0, self.static_chunk_size, -1)
+            attn_mask = mask_to_bias(attn_mask == 1, x.dtype)
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+            hiddens.append(x)  # Save hidden states for skip connections
+            x = downsample(x * mask_down)
+            masks.append(mask_down[:, :, ::2])
+        masks = masks[:-1]
+        mask_mid = masks[-1]
+
+        for resnet, transformer_blocks in self.mid_blocks:
+            x = resnet(x, mask_mid, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            # attn_mask = torch.matmul(mask_mid.transpose(1, 2).contiguous(), mask_mid)
+            attn_mask = add_optional_chunk_mask(x, mask_mid.bool(), False, False, 0, self.static_chunk_size, -1)
+            attn_mask = mask_to_bias(attn_mask == 1, x.dtype)
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+
+        for resnet, transformer_blocks, upsample in self.up_blocks:
+            mask_up = masks.pop()
+            skip = hiddens.pop()
+            x = pack([x[:, :, :skip.shape[-1]], skip], "b * t")[0]
+            x = resnet(x, mask_up, t)
+            x = rearrange(x, "b c t -> b t c").contiguous()
+            # attn_mask = torch.matmul(mask_up.transpose(1, 2).contiguous(), mask_up)
+            attn_mask = add_optional_chunk_mask(x, mask_up.bool(), False, False, 0, self.static_chunk_size, -1)
+            attn_mask = mask_to_bias(attn_mask == 1, x.dtype)
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=attn_mask,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t").contiguous()
+            x = upsample(x * mask_up)
+        x = self.final_block(x, mask_up)
+        output = self.final_proj(x * mask_up)
+        return output * mask

chatterbox/models/s3gen/f0_predictor.py

@@ -0,0 +1,55 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+import torch.nn as nn
+from torch.nn.utils.parametrizations import weight_norm
+
+
+class ConvRNNF0Predictor(nn.Module):
+    def __init__(self,
+                 num_class: int = 1,
+                 in_channels: int = 80,
+                 cond_channels: int = 512
+                 ):
+        super().__init__()
+
+        self.num_class = num_class
+        self.condnet = nn.Sequential(
+            weight_norm(
+                nn.Conv1d(in_channels, cond_channels, kernel_size=3, padding=1)
+            ),
+            nn.ELU(),
+            weight_norm(
+                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+            ),
+            nn.ELU(),
+            weight_norm(
+                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+            ),
+            nn.ELU(),
+            weight_norm(
+                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+            ),
+            nn.ELU(),
+            weight_norm(
+                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
+            ),
+            nn.ELU(),
+        )
+        self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.condnet(x)
+        x = x.transpose(1, 2)
+        return torch.abs(self.classifier(x).squeeze(-1))

chatterbox/models/s3gen/flow.py

@@ -0,0 +1,282 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import logging
+import random
+from typing import Dict, Optional
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from .utils.mask import make_pad_mask
+from .configs import CFM_PARAMS
+
+
+class MaskedDiffWithXvec(torch.nn.Module):
+    def __init__(
+        self,
+        input_size: int = 512,
+        output_size: int = 80,
+        spk_embed_dim: int = 192,
+        output_type: str = "mel",
+        vocab_size: int = 4096,
+        input_frame_rate: int = 50,
+        only_mask_loss: bool = True,
+        encoder: torch.nn.Module = None,
+        length_regulator: torch.nn.Module = None,
+        decoder: torch.nn.Module = None,
+        decoder_conf: Dict = {
+            'in_channels': 240,
+            'out_channel': 80,
+            'spk_emb_dim': 80,
+            'n_spks': 1,
+            'cfm_params': CFM_PARAMS,
+            'decoder_params': {
+                'channels': [256, 256],
+                'dropout': 0.0,
+                'attention_head_dim': 64,
+                'n_blocks': 4,
+                'num_mid_blocks': 12,
+                'num_heads': 8,
+                'act_fn': 'gelu',
+            }
+        },
+        mel_feat_conf: Dict = {
+            'n_fft': 1024,
+            'num_mels': 80,
+            'sampling_rate': 22050,
+            'hop_size': 256,
+            'win_size': 1024,
+            'fmin': 0,
+            'fmax': 8000
+        }
+    ):
+        super().__init__()
+        self.input_size = input_size
+        self.output_size = output_size
+        self.decoder_conf = decoder_conf
+        self.mel_feat_conf = mel_feat_conf
+        self.vocab_size = vocab_size
+        self.output_type = output_type
+        self.input_frame_rate = input_frame_rate
+        logging.info(f"input frame rate={self.input_frame_rate}")
+        self.input_embedding = nn.Embedding(vocab_size, input_size)
+        self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
+        self.encoder = encoder
+        self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
+        self.decoder = decoder
+        self.length_regulator = length_regulator
+        self.only_mask_loss = only_mask_loss
+
+    def forward(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        token = batch['speech_token'].to(device)
+        token_len = batch['speech_token_len'].to(device)
+        feat = batch['speech_feat'].to(device)
+        feat_len = batch['speech_feat_len'].to(device)
+        embedding = batch['embedding'].to(device)
+
+        # xvec projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+
+        # concat text and prompt_text
+        mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
+        token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+        # text encode
+        h, h_lengths = self.encoder(token, token_len)
+        h = self.encoder_proj(h)
+        h, h_lengths = self.length_regulator(h, feat_len)
+
+        # get conditions
+        conds = torch.zeros(feat.shape, device=token.device)
+        for i, j in enumerate(feat_len):
+            if random.random() < 0.5:
+                continue
+            index = random.randint(0, int(0.3 * j))
+            conds[i, :index] = feat[i, :index]
+        conds = conds.transpose(1, 2)
+
+        mask = (~make_pad_mask(feat_len)).to(h)
+        feat = F.interpolate(feat.unsqueeze(dim=1), size=h.shape[1:], mode="nearest").squeeze(dim=1)
+        loss, _ = self.decoder.compute_loss(
+            feat.transpose(1, 2).contiguous(),
+            mask.unsqueeze(1),
+            h.transpose(1, 2).contiguous(),
+            embedding,
+            cond=conds
+        )
+        return {'loss': loss}
+
+    @torch.inference_mode()
+    def inference(self,
+                  token,
+                  token_len,
+                  prompt_token,
+                  prompt_token_len,
+                  prompt_feat,
+                  prompt_feat_len,
+                  embedding,
+                  flow_cache):
+        if self.fp16 is True:
+            prompt_feat = prompt_feat.half()
+            embedding = embedding.half()
+
+        assert token.shape[0] == 1
+        # xvec projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+
+        # concat text and prompt_text
+        token_len1, token_len2 = prompt_token.shape[1], token.shape[1]
+        token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
+        mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
+        token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+        # text encode
+        h, h_lengths = self.encoder(token, token_len)
+        h = self.encoder_proj(h)
+        mel_len1, mel_len2 = prompt_feat.shape[1], int(token_len2 / self.input_frame_rate * 22050 / 256)
+        h, h_lengths = self.length_regulator.inference(h[:, :token_len1], h[:, token_len1:], mel_len1, mel_len2, self.input_frame_rate)
+
+        # get conditions
+        conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
+        conds[:, :mel_len1] = prompt_feat
+        conds = conds.transpose(1, 2)
+
+        mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
+        feat, flow_cache = self.decoder(
+            mu=h.transpose(1, 2).contiguous(),
+            mask=mask.unsqueeze(1),
+            spks=embedding,
+            cond=conds,
+            n_timesteps=10,
+            prompt_len=mel_len1,
+            flow_cache=flow_cache
+        )
+        feat = feat[:, :, mel_len1:]
+        assert feat.shape[2] == mel_len2
+        return feat.float(), flow_cache
+
+
+class CausalMaskedDiffWithXvec(torch.nn.Module):
+    def __init__(
+        self,
+        input_size: int = 512,
+        output_size: int = 80,
+        spk_embed_dim: int = 192,
+        output_type: str = "mel",
+        vocab_size: int = 6561,
+        input_frame_rate: int = 25,
+        only_mask_loss: bool = True,
+        token_mel_ratio: int = 2,
+        pre_lookahead_len: int = 3,
+        encoder: torch.nn.Module = None,
+        decoder: torch.nn.Module = None,
+        decoder_conf: Dict = {
+            'in_channels': 240,
+            'out_channel': 80,
+            'spk_emb_dim': 80,
+            'n_spks': 1,
+            'cfm_params': CFM_PARAMS,
+            'decoder_params': {
+                'channels': [256, 256],
+                'dropout': 0.0,
+                'attention_head_dim': 64,
+                'n_blocks': 4,
+                'num_mid_blocks': 12,
+                'num_heads': 8,
+                'act_fn': 'gelu',
+            }
+        },
+        mel_feat_conf: Dict = {
+            'n_fft': 1024,
+            'num_mels': 80,
+            'sampling_rate': 22050,
+            'hop_size': 256,
+            'win_size': 1024,
+            'fmin': 0,
+            'fmax': 8000
+        }
+    ):
+        super().__init__()
+        self.input_size = input_size
+        self.output_size = output_size
+        self.decoder_conf = decoder_conf
+        self.mel_feat_conf = mel_feat_conf
+        self.vocab_size = vocab_size
+        self.output_type = output_type
+        self.input_frame_rate = input_frame_rate
+        logging.info(f"input frame rate={self.input_frame_rate}")
+        self.input_embedding = nn.Embedding(vocab_size, input_size)
+        self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
+        self.encoder = encoder
+        self.encoder_proj = torch.nn.Linear(self.encoder.output_size(), output_size)
+        self.decoder = decoder
+        self.only_mask_loss = only_mask_loss
+        self.token_mel_ratio = token_mel_ratio
+        self.pre_lookahead_len = pre_lookahead_len
+
+        # FIXME: this was missing - just putting it in as false
+        self.fp16 = False
+
+    @torch.inference_mode()
+    def inference(self,
+                  token,
+                  token_len,
+                  prompt_token,
+                  prompt_token_len,
+                  prompt_feat,
+                  prompt_feat_len,
+                  embedding,
+                  finalize):
+        if self.fp16 is True:
+            prompt_feat = prompt_feat.half()
+            embedding = embedding.half()
+
+        assert token.shape[0] == 1
+        # xvec projection
+        embedding = F.normalize(embedding, dim=1)
+        embedding = self.spk_embed_affine_layer(embedding)
+
+        # concat text and prompt_text
+        token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
+        mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
+        token = self.input_embedding(torch.clamp(token, min=0)) * mask
+
+        # text encode
+        h, h_lengths = self.encoder(token, token_len)
+        if finalize is False:
+            h = h[:, :-self.pre_lookahead_len * self.token_mel_ratio]
+        mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
+        h = self.encoder_proj(h)
+
+        # get conditions
+        conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
+        conds[:, :mel_len1] = prompt_feat
+        conds = conds.transpose(1, 2)
+
+        mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
+        feat, _ = self.decoder(
+            mu=h.transpose(1, 2).contiguous(),
+            mask=mask.unsqueeze(1),
+            spks=embedding,
+            cond=conds,
+            n_timesteps=10
+        )
+        feat = feat[:, :, mel_len1:]
+        assert feat.shape[2] == mel_len2
+        return feat.float(), None  # NOTE jrm: why are they returning None here?

chatterbox/models/s3gen/flow_matching.py

@@ -0,0 +1,218 @@
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import threading
+import torch
+import torch.nn.functional as F
+from .matcha.flow_matching import BASECFM
+from .configs import CFM_PARAMS
+
+
+class ConditionalCFM(BASECFM):
+    def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
+        super().__init__(
+            n_feats=in_channels,
+            cfm_params=cfm_params,
+            n_spks=n_spks,
+            spk_emb_dim=spk_emb_dim,
+        )
+        self.t_scheduler = cfm_params.t_scheduler
+        self.training_cfg_rate = cfm_params.training_cfg_rate
+        self.inference_cfg_rate = cfm_params.inference_cfg_rate
+        in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0)
+        # Just change the architecture of the estimator here
+        self.estimator = estimator
+        self.lock = threading.Lock()
+
+    @torch.inference_mode()
+    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, flow_cache=torch.zeros(1, 80, 0, 2)):
+        """Forward diffusion
+
+        Args:
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            n_timesteps (int): number of diffusion steps
+            temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+
+        Returns:
+            sample: generated mel-spectrogram
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+
+        z = torch.randn_like(mu).to(mu.device).to(mu.dtype) * temperature
+        cache_size = flow_cache.shape[2]
+        # fix prompt and overlap part mu and z
+        if cache_size != 0:
+            z[:, :, :cache_size] = flow_cache[:, :, :, 0]
+            mu[:, :, :cache_size] = flow_cache[:, :, :, 1]
+        z_cache = torch.concat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2)
+        mu_cache = torch.concat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2)
+        flow_cache = torch.stack([z_cache, mu_cache], dim=-1)
+
+        t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
+        if self.t_scheduler == 'cosine':
+            t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
+        return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), flow_cache
+
+    def solve_euler(self, x, t_span, mu, mask, spks, cond):
+        """
+        Fixed euler solver for ODEs.
+        Args:
+            x (torch.Tensor): random noise
+            t_span (torch.Tensor): n_timesteps interpolated
+                shape: (n_timesteps + 1,)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+        """
+        t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
+        t = t.unsqueeze(dim=0)
+
+        # I am storing this because I can later plot it by putting a debugger here and saving it to a file
+        # Or in future might add like a return_all_steps flag
+        sol = []
+
+        # Do not use concat, it may cause memory format changed and trt infer with wrong results!
+        x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
+        mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=x.dtype)
+        mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
+        t_in = torch.zeros([2], device=x.device, dtype=x.dtype)
+        spks_in = torch.zeros([2, 80], device=x.device, dtype=x.dtype)
+        cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=x.dtype)
+        for step in range(1, len(t_span)):
+            # Classifier-Free Guidance inference introduced in VoiceBox
+            x_in[:] = x
+            mask_in[:] = mask
+            mu_in[0] = mu
+            t_in[:] = t.unsqueeze(0)
+            spks_in[0] = spks
+            cond_in[0] = cond
+            dphi_dt = self.forward_estimator(
+                x_in, mask_in,
+                mu_in, t_in,
+                spks_in,
+                cond_in
+            )
+            dphi_dt, cfg_dphi_dt = torch.split(dphi_dt, [x.size(0), x.size(0)], dim=0)
+            dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt)
+            x = x + dt * dphi_dt
+            t = t + dt
+            sol.append(x)
+            if step < len(t_span) - 1:
+                dt = t_span[step + 1] - t
+
+        return sol[-1].float()
+
+    def forward_estimator(self, x, mask, mu, t, spks, cond):
+        if isinstance(self.estimator, torch.nn.Module):
+            return self.estimator.forward(x, mask, mu, t, spks, cond)
+        else:
+            with self.lock:
+                self.estimator.set_input_shape('x', (2, 80, x.size(2)))
+                self.estimator.set_input_shape('mask', (2, 1, x.size(2)))
+                self.estimator.set_input_shape('mu', (2, 80, x.size(2)))
+                self.estimator.set_input_shape('t', (2,))
+                self.estimator.set_input_shape('spks', (2, 80))
+                self.estimator.set_input_shape('cond', (2, 80, x.size(2)))
+                # run trt engine
+                self.estimator.execute_v2([x.contiguous().data_ptr(),
+                                           mask.contiguous().data_ptr(),
+                                           mu.contiguous().data_ptr(),
+                                           t.contiguous().data_ptr(),
+                                           spks.contiguous().data_ptr(),
+                                           cond.contiguous().data_ptr(),
+                                           x.data_ptr()])
+            return x
+
+    def compute_loss(self, x1, mask, mu, spks=None, cond=None):
+        """Computes diffusion loss
+
+        Args:
+            x1 (torch.Tensor): Target
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): target mask
+                shape: (batch_size, 1, mel_timesteps)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            spks (torch.Tensor, optional): speaker embedding. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+
+        Returns:
+            loss: conditional flow matching loss
+            y: conditional flow
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        b, _, t = mu.shape
+
+        # random timestep
+        t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype)
+        if self.t_scheduler == 'cosine':
+            t = 1 - torch.cos(t * 0.5 * torch.pi)
+        # sample noise p(x_0)
+        z = torch.randn_like(x1)
+
+        y = (1 - (1 - self.sigma_min) * t) * z + t * x1
+        u = x1 - (1 - self.sigma_min) * z
+
+        # during training, we randomly drop condition to trade off mode coverage and sample fidelity
+        if self.training_cfg_rate > 0:
+            cfg_mask = torch.rand(b, device=x1.device) > self.training_cfg_rate
+            mu = mu * cfg_mask.view(-1, 1, 1)
+            spks = spks * cfg_mask.view(-1, 1)
+            cond = cond * cfg_mask.view(-1, 1, 1)
+
+        pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond)
+        loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1])
+        return loss, y
+
+
+class CausalConditionalCFM(ConditionalCFM):
+    def __init__(self, in_channels=240, cfm_params=CFM_PARAMS, n_spks=1, spk_emb_dim=80, estimator=None):
+        super().__init__(in_channels, cfm_params, n_spks, spk_emb_dim, estimator)
+        self.rand_noise = torch.randn([1, 80, 50 * 300])
+
+    @torch.inference_mode()
+    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
+        """Forward diffusion
+
+        Args:
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            n_timesteps (int): number of diffusion steps
+            temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+
+        Returns:
+            sample: generated mel-spectrogram
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+
+        z = self.rand_noise[:, :, :mu.size(2)].to(mu.device).to(mu.dtype) * temperature
+        # fix prompt and overlap part mu and z
+        t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
+        if self.t_scheduler == 'cosine':
+            t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
+        return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), None

chatterbox/models/s3gen/hifigan.py

@@ -0,0 +1,474 @@
+# jrm: adapted from CosyVoice/cosyvoice/hifigan/generator.py
+#      most modules should be reusable, but I found their SineGen changed a git.
+
+# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""HIFI-GAN"""
+
+from typing import Dict, Optional, List
+import numpy as np
+from scipy.signal import get_window
+import torch
+import torch.nn.functional as F
+from torch.nn import Conv1d
+from torch.nn import ConvTranspose1d
+from torch.nn.utils import remove_weight_norm
+from torch.nn.utils.parametrizations import weight_norm
+from torch.distributions.uniform import Uniform
+from torch import nn, sin, pow
+from torch.nn import Parameter
+
+
+class Snake(nn.Module):
+    '''
+    Implementation of a sine-based periodic activation function
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter
+    References:
+        - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snake(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    '''
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+        '''
+        Initialization.
+        INPUT:
+            - in_features: shape of the input
+            - alpha: trainable parameter
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            alpha will be trained along with the rest of your model.
+        '''
+        super(Snake, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale: # log scale alphas initialized to zeros
+            self.alpha = Parameter(torch.zeros(in_features) * alpha)
+        else: # linear scale alphas initialized to ones
+            self.alpha = Parameter(torch.ones(in_features) * alpha)
+
+        self.alpha.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        '''
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        Snake ∶= x + 1/a * sin^2 (xa)
+        '''
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+        x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x
+
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+"""hifigan based generator implementation.
+
+This code is modified from https://github.com/jik876/hifi-gan
+ ,https://github.com/kan-bayashi/ParallelWaveGAN and
+ https://github.com/NVIDIA/BigVGAN
+
+"""
+
+
+class ResBlock(torch.nn.Module):
+    """Residual block module in HiFiGAN/BigVGAN."""
+    def __init__(
+        self,
+        channels: int = 512,
+        kernel_size: int = 3,
+        dilations: List[int] = [1, 3, 5],
+    ):
+        super(ResBlock, self).__init__()
+        self.convs1 = nn.ModuleList()
+        self.convs2 = nn.ModuleList()
+
+        for dilation in dilations:
+            self.convs1.append(
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation,
+                        padding=get_padding(kernel_size, dilation)
+                    )
+                )
+            )
+            self.convs2.append(
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1)
+                    )
+                )
+            )
+        self.convs1.apply(init_weights)
+        self.convs2.apply(init_weights)
+        self.activations1 = nn.ModuleList([
+            Snake(channels, alpha_logscale=False)
+            for _ in range(len(self.convs1))
+        ])
+        self.activations2 = nn.ModuleList([
+            Snake(channels, alpha_logscale=False)
+            for _ in range(len(self.convs2))
+        ])
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        for idx in range(len(self.convs1)):
+            xt = self.activations1[idx](x)
+            xt = self.convs1[idx](xt)
+            xt = self.activations2[idx](xt)
+            xt = self.convs2[idx](xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for idx in range(len(self.convs1)):
+            remove_weight_norm(self.convs1[idx])
+            remove_weight_norm(self.convs2[idx])
+
+
+class SineGen(torch.nn.Module):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(self, samp_rate, harmonic_num=0,
+                 sine_amp=0.1, noise_std=0.003,
+                 voiced_threshold=0):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = (f0 > self.voiced_threshold).type(torch.float32)
+        return uv
+
+    @torch.no_grad()
+    def forward(self, f0):
+        """
+        :param f0: [B, 1, sample_len], Hz
+        :return: [B, 1, sample_len]
+        """
+
+        F_mat = torch.zeros((f0.size(0), self.harmonic_num + 1, f0.size(-1))).to(f0.device)
+        for i in range(self.harmonic_num + 1):
+            F_mat[:, i: i + 1, :] = f0 * (i + 1) / self.sampling_rate
+
+        theta_mat = 2 * np.pi * (torch.cumsum(F_mat, dim=-1) % 1)
+        u_dist = Uniform(low=-np.pi, high=np.pi)
+        phase_vec = u_dist.sample(sample_shape=(f0.size(0), self.harmonic_num + 1, 1)).to(F_mat.device)
+        phase_vec[:, 0, :] = 0
+
+        # generate sine waveforms
+        sine_waves = self.sine_amp * torch.sin(theta_mat + phase_vec)
+
+        # generate uv signal
+        uv = self._f02uv(f0)
+
+        # noise: for unvoiced should be similar to sine_amp
+        #        std = self.sine_amp/3 -> max value ~ self.sine_amp
+        # .       for voiced regions is self.noise_std
+        noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+        noise = noise_amp * torch.randn_like(sine_waves)
+
+        # first: set the unvoiced part to 0 by uv
+        # then: additive noise
+        sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+    """ SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0):
+        super(SourceModuleHnNSF, self).__init__()
+
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+
+        # to produce sine waveforms
+        self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
+                                 sine_amp, add_noise_std, voiced_threshod)
+
+        # to merge source harmonics into a single excitation
+        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+        self.l_tanh = torch.nn.Tanh()
+
+    def forward(self, x):
+        """
+        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+        F0_sampled (batchsize, length, 1)
+        Sine_source (batchsize, length, 1)
+        noise_source (batchsize, length 1)
+        """
+        # source for harmonic branch
+        with torch.no_grad():
+            sine_wavs, uv, _ = self.l_sin_gen(x.transpose(1, 2))
+            sine_wavs = sine_wavs.transpose(1, 2)
+            uv = uv.transpose(1, 2)
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+
+        # source for noise branch, in the same shape as uv
+        noise = torch.randn_like(uv) * self.sine_amp / 3
+        return sine_merge, noise, uv
+
+
+class HiFTGenerator(nn.Module):
+    """
+    HiFTNet Generator: Neural Source Filter + ISTFTNet
+    https://arxiv.org/abs/2309.09493
+    """
+    def __init__(
+            self,
+            in_channels: int = 80,
+            base_channels: int = 512,
+            nb_harmonics: int = 8,
+            sampling_rate: int = 22050,
+            nsf_alpha: float = 0.1,
+            nsf_sigma: float = 0.003,
+            nsf_voiced_threshold: float = 10,
+            upsample_rates: List[int] = [8, 8],
+            upsample_kernel_sizes: List[int] = [16, 16],
+            istft_params: Dict[str, int] = {"n_fft": 16, "hop_len": 4},
+            resblock_kernel_sizes: List[int] = [3, 7, 11],
+            resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+            source_resblock_kernel_sizes: List[int] = [7, 11],
+            source_resblock_dilation_sizes: List[List[int]] = [[1, 3, 5], [1, 3, 5]],
+            lrelu_slope: float = 0.1,
+            audio_limit: float = 0.99,
+            f0_predictor: torch.nn.Module = None,
+    ):
+        super(HiFTGenerator, self).__init__()
+
+        self.out_channels = 1
+        self.nb_harmonics = nb_harmonics
+        self.sampling_rate = sampling_rate
+        self.istft_params = istft_params
+        self.lrelu_slope = lrelu_slope
+        self.audio_limit = audio_limit
+
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.m_source = SourceModuleHnNSF(
+            sampling_rate=sampling_rate,
+            upsample_scale=np.prod(upsample_rates) * istft_params["hop_len"],
+            harmonic_num=nb_harmonics,
+            sine_amp=nsf_alpha,
+            add_noise_std=nsf_sigma,
+            voiced_threshod=nsf_voiced_threshold)
+        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * istft_params["hop_len"])
+
+        self.conv_pre = weight_norm(
+            Conv1d(in_channels, base_channels, 7, 1, padding=3)
+        )
+
+        # Up
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        base_channels // (2**i),
+                        base_channels // (2**(i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        # Down
+        self.source_downs = nn.ModuleList()
+        self.source_resblocks = nn.ModuleList()
+        downsample_rates = [1] + upsample_rates[::-1][:-1]
+        downsample_cum_rates = np.cumprod(downsample_rates)
+        for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1], source_resblock_kernel_sizes, source_resblock_dilation_sizes)):
+            if u == 1:
+                self.source_downs.append(
+                    Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), 1, 1)
+                )
+            else:
+                self.source_downs.append(
+                    Conv1d(istft_params["n_fft"] + 2, base_channels // (2 ** (i + 1)), u * 2, u, padding=(u // 2))
+                )
+
+            self.source_resblocks.append(
+                ResBlock(base_channels // (2 ** (i + 1)), k, d)
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = base_channels // (2**(i + 1))
+            for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(ResBlock(ch, k, d))
+
+        self.conv_post = weight_norm(Conv1d(ch, istft_params["n_fft"] + 2, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+        self.reflection_pad = nn.ReflectionPad1d((1, 0))
+        self.stft_window = torch.from_numpy(get_window("hann", istft_params["n_fft"], fftbins=True).astype(np.float32))
+        self.f0_predictor = f0_predictor
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
+        self.m_source.remove_weight_norm()
+        for l in self.source_downs:
+            remove_weight_norm(l)
+        for l in self.source_resblocks:
+            l.remove_weight_norm()
+
+    def _stft(self, x):
+        spec = torch.stft(
+            x,
+            self.istft_params["n_fft"], self.istft_params["hop_len"], self.istft_params["n_fft"], window=self.stft_window.to(x.device),
+            return_complex=True)
+        spec = torch.view_as_real(spec)  # [B, F, TT, 2]
+        return spec[..., 0], spec[..., 1]
+
+    def _istft(self, magnitude, phase):
+        magnitude = torch.clip(magnitude, max=1e2)
+        real = magnitude * torch.cos(phase)
+        img = magnitude * torch.sin(phase)
+        inverse_transform = torch.istft(torch.complex(real, img), self.istft_params["n_fft"], self.istft_params["hop_len"],
+                                        self.istft_params["n_fft"], window=self.stft_window.to(magnitude.device))
+        return inverse_transform
+
+    def decode(self, x: torch.Tensor, s: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor:
+        s_stft_real, s_stft_imag = self._stft(s.squeeze(1))
+        s_stft = torch.cat([s_stft_real, s_stft_imag], dim=1)
+
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, self.lrelu_slope)
+            x = self.ups[i](x)
+
+            if i == self.num_upsamples - 1:
+                x = self.reflection_pad(x)
+
+            # fusion
+            si = self.source_downs[i](s_stft)
+            si = self.source_resblocks[i](si)
+            x = x + si
+
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        magnitude = torch.exp(x[:, :self.istft_params["n_fft"] // 2 + 1, :])
+        phase = torch.sin(x[:, self.istft_params["n_fft"] // 2 + 1:, :])  # actually, sin is redundancy
+
+        x = self._istft(magnitude, phase)
+        x = torch.clamp(x, -self.audio_limit, self.audio_limit)
+        return x
+
+    def forward(
+            self,
+            batch: dict,
+            device: torch.device,
+    ) -> Dict[str, Optional[torch.Tensor]]:
+        speech_feat = batch['speech_feat'].transpose(1, 2).to(device)
+        # mel->f0
+        f0 = self.f0_predictor(speech_feat)
+        # f0->source
+        s = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
+        s, _, _ = self.m_source(s)
+        s = s.transpose(1, 2)
+        # mel+source->speech
+        generated_speech = self.decode(x=speech_feat, s=s)
+        return generated_speech, f0
+
+    @torch.inference_mode()
+    def inference(self, speech_feat: torch.Tensor, cache_source: torch.Tensor = torch.zeros(1, 1, 0)) -> torch.Tensor:
+        # mel->f0
+        f0 = self.f0_predictor(speech_feat)
+        # f0->source
+        s = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t
+        s, _, _ = self.m_source(s)
+        s = s.transpose(1, 2)
+        # use cache_source to avoid glitch
+        if cache_source.shape[2] != 0:
+            s[:, :, :cache_source.shape[2]] = cache_source
+        generated_speech = self.decode(x=speech_feat, s=s)
+        return generated_speech, s

chatterbox/models/s3gen/matcha/decoder.py

@@ -0,0 +1,443 @@
+import math
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from conformer import ConformerBlock
+from diffusers.models.activations import get_activation
+from einops import pack, rearrange, repeat
+
+from .transformer import BasicTransformerBlock
+
+
+class SinusoidalPosEmb(torch.nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+        assert self.dim % 2 == 0, "SinusoidalPosEmb requires dim to be even"
+
+    def forward(self, x, scale=1000):
+        if x.ndim < 1:
+            x = x.unsqueeze(0)
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
+        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+class Block1D(torch.nn.Module):
+    def __init__(self, dim, dim_out, groups=8):
+        super().__init__()
+        self.block = torch.nn.Sequential(
+            torch.nn.Conv1d(dim, dim_out, 3, padding=1),
+            torch.nn.GroupNorm(groups, dim_out),
+            nn.Mish(),
+        )
+
+    def forward(self, x, mask):
+        output = self.block(x * mask)
+        return output * mask
+
+
+class ResnetBlock1D(torch.nn.Module):
+    def __init__(self, dim, dim_out, time_emb_dim, groups=8):
+        super().__init__()
+        self.mlp = torch.nn.Sequential(nn.Mish(), torch.nn.Linear(time_emb_dim, dim_out))
+
+        self.block1 = Block1D(dim, dim_out, groups=groups)
+        self.block2 = Block1D(dim_out, dim_out, groups=groups)
+
+        self.res_conv = torch.nn.Conv1d(dim, dim_out, 1)
+
+    def forward(self, x, mask, time_emb):
+        h = self.block1(x, mask)
+        h += self.mlp(time_emb).unsqueeze(-1)
+        h = self.block2(h, mask)
+        output = h + self.res_conv(x * mask)
+        return output
+
+
+class Downsample1D(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.conv = torch.nn.Conv1d(dim, dim, 3, 2, 1)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        act_fn: str = "silu",
+        out_dim: int = None,
+        post_act_fn: Optional[str] = None,
+        cond_proj_dim=None,
+    ):
+        super().__init__()
+
+        self.linear_1 = nn.Linear(in_channels, time_embed_dim)
+
+        if cond_proj_dim is not None:
+            self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
+        else:
+            self.cond_proj = None
+
+        self.act = get_activation(act_fn)
+
+        if out_dim is not None:
+            time_embed_dim_out = out_dim
+        else:
+            time_embed_dim_out = time_embed_dim
+        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out)
+
+        if post_act_fn is None:
+            self.post_act = None
+        else:
+            self.post_act = get_activation(post_act_fn)
+
+    def forward(self, sample, condition=None):
+        if condition is not None:
+            sample = sample + self.cond_proj(condition)
+        sample = self.linear_1(sample)
+
+        if self.act is not None:
+            sample = self.act(sample)
+
+        sample = self.linear_2(sample)
+
+        if self.post_act is not None:
+            sample = self.post_act(sample)
+        return sample
+
+
+class Upsample1D(nn.Module):
+    """A 1D upsampling layer with an optional convolution.
+
+    Parameters:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        use_conv_transpose (`bool`, default `False`):
+            option to use a convolution transpose.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
+    """
+
+    def __init__(self, channels, use_conv=False, use_conv_transpose=True, out_channels=None, name="conv"):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_conv_transpose = use_conv_transpose
+        self.name = name
+
+        self.conv = None
+        if use_conv_transpose:
+            self.conv = nn.ConvTranspose1d(channels, self.out_channels, 4, 2, 1)
+        elif use_conv:
+            self.conv = nn.Conv1d(self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, inputs):
+        assert inputs.shape[1] == self.channels
+        if self.use_conv_transpose:
+            return self.conv(inputs)
+
+        outputs = F.interpolate(inputs, scale_factor=2.0, mode="nearest")
+
+        if self.use_conv:
+            outputs = self.conv(outputs)
+
+        return outputs
+
+
+class ConformerWrapper(ConformerBlock):
+    def __init__(  # pylint: disable=useless-super-delegation
+        self,
+        *,
+        dim,
+        dim_head=64,
+        heads=8,
+        ff_mult=4,
+        conv_expansion_factor=2,
+        conv_kernel_size=31,
+        attn_dropout=0,
+        ff_dropout=0,
+        conv_dropout=0,
+        conv_causal=False,
+    ):
+        super().__init__(
+            dim=dim,
+            dim_head=dim_head,
+            heads=heads,
+            ff_mult=ff_mult,
+            conv_expansion_factor=conv_expansion_factor,
+            conv_kernel_size=conv_kernel_size,
+            attn_dropout=attn_dropout,
+            ff_dropout=ff_dropout,
+            conv_dropout=conv_dropout,
+            conv_causal=conv_causal,
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        timestep=None,
+    ):
+        return super().forward(x=hidden_states, mask=attention_mask.bool())
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        channels=(256, 256),
+        dropout=0.05,
+        attention_head_dim=64,
+        n_blocks=1,
+        num_mid_blocks=2,
+        num_heads=4,
+        act_fn="snake",
+        down_block_type="transformer",
+        mid_block_type="transformer",
+        up_block_type="transformer",
+    ):
+        super().__init__()
+        channels = tuple(channels)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        self.time_embeddings = SinusoidalPosEmb(in_channels)
+        time_embed_dim = channels[0] * 4
+        self.time_mlp = TimestepEmbedding(
+            in_channels=in_channels,
+            time_embed_dim=time_embed_dim,
+            act_fn="silu",
+        )
+
+        self.down_blocks = nn.ModuleList([])
+        self.mid_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        output_channel = in_channels
+        for i in range(len(channels)):  # pylint: disable=consider-using-enumerate
+            input_channel = output_channel
+            output_channel = channels[i]
+            is_last = i == len(channels) - 1
+            resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+            transformer_blocks = nn.ModuleList(
+                [
+                    self.get_block(
+                        down_block_type,
+                        output_channel,
+                        attention_head_dim,
+                        num_heads,
+                        dropout,
+                        act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            downsample = (
+                Downsample1D(output_channel) if not is_last else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+            )
+
+            self.down_blocks.append(nn.ModuleList([resnet, transformer_blocks, downsample]))
+
+        for i in range(num_mid_blocks):
+            input_channel = channels[-1]
+            out_channels = channels[-1]
+
+            resnet = ResnetBlock1D(dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim)
+
+            transformer_blocks = nn.ModuleList(
+                [
+                    self.get_block(
+                        mid_block_type,
+                        output_channel,
+                        attention_head_dim,
+                        num_heads,
+                        dropout,
+                        act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+
+            self.mid_blocks.append(nn.ModuleList([resnet, transformer_blocks]))
+
+        channels = channels[::-1] + (channels[0],)
+        for i in range(len(channels) - 1):
+            input_channel = channels[i]
+            output_channel = channels[i + 1]
+            is_last = i == len(channels) - 2
+
+            resnet = ResnetBlock1D(
+                dim=2 * input_channel,
+                dim_out=output_channel,
+                time_emb_dim=time_embed_dim,
+            )
+            transformer_blocks = nn.ModuleList(
+                [
+                    self.get_block(
+                        up_block_type,
+                        output_channel,
+                        attention_head_dim,
+                        num_heads,
+                        dropout,
+                        act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            upsample = (
+                Upsample1D(output_channel, use_conv_transpose=True)
+                if not is_last
+                else nn.Conv1d(output_channel, output_channel, 3, padding=1)
+            )
+
+            self.up_blocks.append(nn.ModuleList([resnet, transformer_blocks, upsample]))
+
+        self.final_block = Block1D(channels[-1], channels[-1])
+        self.final_proj = nn.Conv1d(channels[-1], self.out_channels, 1)
+
+        self.initialize_weights()
+        # nn.init.normal_(self.final_proj.weight)
+
+    @staticmethod
+    def get_block(block_type, dim, attention_head_dim, num_heads, dropout, act_fn):
+        if block_type == "conformer":
+            block = ConformerWrapper(
+                dim=dim,
+                dim_head=attention_head_dim,
+                heads=num_heads,
+                ff_mult=1,
+                conv_expansion_factor=2,
+                ff_dropout=dropout,
+                attn_dropout=dropout,
+                conv_dropout=dropout,
+                conv_kernel_size=31,
+            )
+        elif block_type == "transformer":
+            block = BasicTransformerBlock(
+                dim=dim,
+                num_attention_heads=num_heads,
+                attention_head_dim=attention_head_dim,
+                dropout=dropout,
+                activation_fn=act_fn,
+            )
+        else:
+            raise ValueError(f"Unknown block type {block_type}")
+
+        return block
+
+    def initialize_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv1d):
+                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.GroupNorm):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+            elif isinstance(m, nn.Linear):
+                nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+
+    def forward(self, x, mask, mu, t, spks=None, cond=None):
+        """Forward pass of the UNet1DConditional model.
+
+        Args:
+            x (torch.Tensor): shape (batch_size, in_channels, time)
+            mask (_type_): shape (batch_size, 1, time)
+            t (_type_): shape (batch_size)
+            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
+            cond (_type_, optional): placeholder for future use. Defaults to None.
+
+        Raises:
+            ValueError: _description_
+            ValueError: _description_
+
+        Returns:
+            _type_: _description_
+        """
+
+        t = self.time_embeddings(t)
+        t = self.time_mlp(t)
+
+        x = pack([x, mu], "b * t")[0]
+
+        if spks is not None:
+            spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
+            x = pack([x, spks], "b * t")[0]
+
+        hiddens = []
+        masks = [mask]
+        for resnet, transformer_blocks, downsample in self.down_blocks:
+            mask_down = masks[-1]
+            x = resnet(x, mask_down, t)
+            x = rearrange(x, "b c t -> b t c")
+            mask_down = rearrange(mask_down, "b 1 t -> b t")
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=mask_down,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t")
+            mask_down = rearrange(mask_down, "b t -> b 1 t")
+            hiddens.append(x)  # Save hidden states for skip connections
+            x = downsample(x * mask_down)
+            masks.append(mask_down[:, :, ::2])
+
+        masks = masks[:-1]
+        mask_mid = masks[-1]
+
+        for resnet, transformer_blocks in self.mid_blocks:
+            x = resnet(x, mask_mid, t)
+            x = rearrange(x, "b c t -> b t c")
+            mask_mid = rearrange(mask_mid, "b 1 t -> b t")
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=mask_mid,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t")
+            mask_mid = rearrange(mask_mid, "b t -> b 1 t")
+
+        for resnet, transformer_blocks, upsample in self.up_blocks:
+            mask_up = masks.pop()
+            x = resnet(pack([x, hiddens.pop()], "b * t")[0], mask_up, t)
+            x = rearrange(x, "b c t -> b t c")
+            mask_up = rearrange(mask_up, "b 1 t -> b t")
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x,
+                    attention_mask=mask_up,
+                    timestep=t,
+                )
+            x = rearrange(x, "b t c -> b c t")
+            mask_up = rearrange(mask_up, "b t -> b 1 t")
+            x = upsample(x * mask_up)
+
+        x = self.final_block(x, mask_up)
+        output = self.final_proj(x * mask_up)
+
+        return output * mask

chatterbox/models/s3gen/matcha/flow_matching.py

@@ -0,0 +1,129 @@
+from abc import ABC
+
+import torch
+import torch.nn.functional as F
+
+from .decoder import Decoder
+
+
+class BASECFM(torch.nn.Module, ABC):
+    def __init__(
+        self,
+        n_feats,
+        cfm_params,
+        n_spks=1,
+        spk_emb_dim=128,
+    ):
+        super().__init__()
+        self.n_feats = n_feats
+        self.n_spks = n_spks
+        self.spk_emb_dim = spk_emb_dim
+        self.solver = cfm_params.solver
+        if hasattr(cfm_params, "sigma_min"):
+            self.sigma_min = cfm_params.sigma_min
+        else:
+            self.sigma_min = 1e-4
+
+        self.estimator = None
+
+    @torch.inference_mode()
+    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
+        """Forward diffusion
+
+        Args:
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            n_timesteps (int): number of diffusion steps
+            temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+
+        Returns:
+            sample: generated mel-spectrogram
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        z = torch.randn_like(mu) * temperature
+        t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device)
+        return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond)
+
+    def solve_euler(self, x, t_span, mu, mask, spks, cond):
+        """
+        Fixed euler solver for ODEs.
+        Args:
+            x (torch.Tensor): random noise
+            t_span (torch.Tensor): n_timesteps interpolated
+                shape: (n_timesteps + 1,)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+        """
+        t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
+
+        # I am storing this because I can later plot it by putting a debugger here and saving it to a file
+        # Or in future might add like a return_all_steps flag
+        sol = []
+
+        for step in range(1, len(t_span)):
+            dphi_dt = self.estimator(x, mask, mu, t, spks, cond)
+
+            x = x + dt * dphi_dt
+            t = t + dt
+            sol.append(x)
+            if step < len(t_span) - 1:
+                dt = t_span[step + 1] - t
+
+        return sol[-1]
+
+    def compute_loss(self, x1, mask, mu, spks=None, cond=None):
+        """Computes diffusion loss
+
+        Args:
+            x1 (torch.Tensor): Target
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): target mask
+                shape: (batch_size, 1, mel_timesteps)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            spks (torch.Tensor, optional): speaker embedding. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+
+        Returns:
+            loss: conditional flow matching loss
+            y: conditional flow
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        b, _, t = mu.shape
+
+        # random timestep
+        t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype)
+        # sample noise p(x_0)
+        z = torch.randn_like(x1)
+
+        y = (1 - (1 - self.sigma_min) * t) * z + t * x1
+        u = x1 - (1 - self.sigma_min) * z
+
+        loss = F.mse_loss(self.estimator(y, mask, mu, t.squeeze(), spks), u, reduction="sum") / (
+            torch.sum(mask) * u.shape[1]
+        )
+        return loss, y
+
+
+class CFM(BASECFM):
+    def __init__(self, in_channels, out_channel, cfm_params, decoder_params, n_spks=1, spk_emb_dim=64):
+        super().__init__(
+            n_feats=in_channels,
+            cfm_params=cfm_params,
+            n_spks=n_spks,
+            spk_emb_dim=spk_emb_dim,
+        )
+
+        in_channels = in_channels + (spk_emb_dim if n_spks > 1 else 0)
+        # Just change the architecture of the estimator here
+        self.estimator = Decoder(in_channels=in_channels, out_channels=out_channel, **decoder_params)

chatterbox/models/s3gen/matcha/text_encoder.py

@@ -0,0 +1,413 @@
+""" from https://github.com/jaywalnut310/glow-tts """
+
+import math
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-4):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = torch.nn.Parameter(torch.ones(channels))
+        self.beta = torch.nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        n_dims = len(x.shape)
+        mean = torch.mean(x, 1, keepdim=True)
+        variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
+
+        x = (x - mean) * torch.rsqrt(variance + self.eps)
+
+        shape = [1, -1] + [1] * (n_dims - 2)
+        x = x * self.gamma.view(*shape) + self.beta.view(*shape)
+        return x
+
+
+class ConvReluNorm(nn.Module):
+    def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_channels = hidden_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+
+        self.conv_layers = torch.nn.ModuleList()
+        self.norm_layers = torch.nn.ModuleList()
+        self.conv_layers.append(torch.nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
+        self.norm_layers.append(LayerNorm(hidden_channels))
+        self.relu_drop = torch.nn.Sequential(torch.nn.ReLU(), torch.nn.Dropout(p_dropout))
+        for _ in range(n_layers - 1):
+            self.conv_layers.append(
+                torch.nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)
+            )
+            self.norm_layers.append(LayerNorm(hidden_channels))
+        self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask):
+        x_org = x
+        for i in range(self.n_layers):
+            x = self.conv_layers[i](x * x_mask)
+            x = self.norm_layers[i](x)
+            x = self.relu_drop(x)
+        x = x_org + self.proj(x)
+        return x * x_mask
+
+
+class DurationPredictor(nn.Module):
+    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
+        super().__init__()
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.p_dropout = p_dropout
+
+        self.drop = torch.nn.Dropout(p_dropout)
+        self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_1 = LayerNorm(filter_channels)
+        self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_2 = LayerNorm(filter_channels)
+        self.proj = torch.nn.Conv1d(filter_channels, 1, 1)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class RotaryPositionalEmbeddings(nn.Module):
+    """
+    ## RoPE module
+
+    Rotary encoding transforms pairs of features by rotating in the 2D plane.
+    That is, it organizes the $d$ features as $\frac{d}{2}$ pairs.
+    Each pair can be considered a coordinate in a 2D plane, and the encoding will rotate it
+    by an angle depending on the position of the token.
+    """
+
+    def __init__(self, d: int, base: int = 10_000):
+        r"""
+        * `d` is the number of features $d$
+        * `base` is the constant used for calculating $\Theta$
+        """
+        super().__init__()
+
+        self.base = base
+        self.d = int(d)
+        self.cos_cached = None
+        self.sin_cached = None
+
+    def _build_cache(self, x: torch.Tensor):
+        r"""
+        Cache $\cos$ and $\sin$ values
+        """
+        # Return if cache is already built
+        if self.cos_cached is not None and x.shape[0] <= self.cos_cached.shape[0]:
+            return
+
+        # Get sequence length
+        seq_len = x.shape[0]
+
+        # $\Theta = {\theta_i = 10000^{-\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
+        theta = 1.0 / (self.base ** (torch.arange(0, self.d, 2).float() / self.d)).to(x.device)
+
+        # Create position indexes `[0, 1, ..., seq_len - 1]`
+        seq_idx = torch.arange(seq_len, device=x.device).float().to(x.device)
+
+        # Calculate the product of position index and $\theta_i$
+        idx_theta = torch.einsum("n,d->nd", seq_idx, theta)
+
+        # Concatenate so that for row $m$ we have
+        # $[m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}, m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}]$
+        idx_theta2 = torch.cat([idx_theta, idx_theta], dim=1)
+
+        # Cache them
+        self.cos_cached = idx_theta2.cos()[:, None, None, :]
+        self.sin_cached = idx_theta2.sin()[:, None, None, :]
+
+    def _neg_half(self, x: torch.Tensor):
+        # $\frac{d}{2}$
+        d_2 = self.d // 2
+
+        # Calculate $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$
+        return torch.cat([-x[:, :, :, d_2:], x[:, :, :, :d_2]], dim=-1)
+
+    def forward(self, x: torch.Tensor):
+        """
+        * `x` is the Tensor at the head of a key or a query with shape `[seq_len, batch_size, n_heads, d]`
+        """
+        # Cache $\cos$ and $\sin$ values
+        x = rearrange(x, "b h t d -> t b h d")
+
+        self._build_cache(x)
+
+        # Split the features, we can choose to apply rotary embeddings only to a partial set of features.
+        x_rope, x_pass = x[..., : self.d], x[..., self.d :]
+
+        # Calculate
+        # $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., x^{(\frac{d}{2})}]$
+        neg_half_x = self._neg_half(x_rope)
+
+        x_rope = (x_rope * self.cos_cached[: x.shape[0]]) + (neg_half_x * self.sin_cached[: x.shape[0]])
+
+        return rearrange(torch.cat((x_rope, x_pass), dim=-1), "t b h d -> b h t d")
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        heads_share=True,
+        p_dropout=0.0,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.heads_share = heads_share
+        self.proximal_bias = proximal_bias
+        self.p_dropout = p_dropout
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = torch.nn.Conv1d(channels, channels, 1)
+        self.conv_k = torch.nn.Conv1d(channels, channels, 1)
+        self.conv_v = torch.nn.Conv1d(channels, channels, 1)
+
+        # from https://nn.labml.ai/transformers/rope/index.html
+        self.query_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5)
+        self.key_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5)
+
+        self.conv_o = torch.nn.Conv1d(channels, out_channels, 1)
+        self.drop = torch.nn.Dropout(p_dropout)
+
+        torch.nn.init.xavier_uniform_(self.conv_q.weight)
+        torch.nn.init.xavier_uniform_(self.conv_k.weight)
+        if proximal_init:
+            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
+            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
+        torch.nn.init.xavier_uniform_(self.conv_v.weight)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = rearrange(query, "b (h c) t-> b h t c", h=self.n_heads)
+        key = rearrange(key, "b (h c) t-> b h t c", h=self.n_heads)
+        value = rearrange(value, "b (h c) t-> b h t c", h=self.n_heads)
+
+        query = self.query_rotary_pe(query)
+        key = self.key_rotary_pe(key)
+
+        scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
+
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+        p_attn = torch.nn.functional.softmax(scores, dim=-1)
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        output = output.transpose(2, 3).contiguous().view(b, d, t_t)
+        return output, p_attn
+
+    @staticmethod
+    def _attention_bias_proximal(length):
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+
+        self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.conv_2 = torch.nn.Conv1d(filter_channels, out_channels, kernel_size, padding=kernel_size // 2)
+        self.drop = torch.nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        return x * x_mask
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        **kwargs,
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+
+        self.drop = torch.nn.Dropout(p_dropout)
+        self.attn_layers = torch.nn.ModuleList()
+        self.norm_layers_1 = torch.nn.ModuleList()
+        self.ffn_layers = torch.nn.ModuleList()
+        self.norm_layers_2 = torch.nn.ModuleList()
+        for _ in range(self.n_layers):
+            self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        for i in range(self.n_layers):
+            x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        encoder_type,
+        encoder_params,
+        duration_predictor_params,
+        n_vocab,
+        n_spks=1,
+        spk_emb_dim=128,
+    ):
+        super().__init__()
+        self.encoder_type = encoder_type
+        self.n_vocab = n_vocab
+        self.n_feats = encoder_params.n_feats
+        self.n_channels = encoder_params.n_channels
+        self.spk_emb_dim = spk_emb_dim
+        self.n_spks = n_spks
+
+        self.emb = torch.nn.Embedding(n_vocab, self.n_channels)
+        torch.nn.init.normal_(self.emb.weight, 0.0, self.n_channels**-0.5)
+
+        if encoder_params.prenet:
+            self.prenet = ConvReluNorm(
+                self.n_channels,
+                self.n_channels,
+                self.n_channels,
+                kernel_size=5,
+                n_layers=3,
+                p_dropout=0.5,
+            )
+        else:
+            self.prenet = lambda x, x_mask: x
+
+        self.encoder = Encoder(
+            encoder_params.n_channels + (spk_emb_dim if n_spks > 1 else 0),
+            encoder_params.filter_channels,
+            encoder_params.n_heads,
+            encoder_params.n_layers,
+            encoder_params.kernel_size,
+            encoder_params.p_dropout,
+        )
+
+        self.proj_m = torch.nn.Conv1d(self.n_channels + (spk_emb_dim if n_spks > 1 else 0), self.n_feats, 1)
+        self.proj_w = DurationPredictor(
+            self.n_channels + (spk_emb_dim if n_spks > 1 else 0),
+            duration_predictor_params.filter_channels_dp,
+            duration_predictor_params.kernel_size,
+            duration_predictor_params.p_dropout,
+        )
+
+    def forward(self, x, x_lengths, spks=None):
+        """Run forward pass to the transformer based encoder and duration predictor
+
+        Args:
+            x (torch.Tensor): text input
+                shape: (batch_size, max_text_length)
+            x_lengths (torch.Tensor): text input lengths
+                shape: (batch_size,)
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size,)
+
+        Returns:
+            mu (torch.Tensor): average output of the encoder
+                shape: (batch_size, n_feats, max_text_length)
+            logw (torch.Tensor): log duration predicted by the duration predictor
+                shape: (batch_size, 1, max_text_length)
+            x_mask (torch.Tensor): mask for the text input
+                shape: (batch_size, 1, max_text_length)
+        """
+        x = self.emb(x) * math.sqrt(self.n_channels)
+        x = torch.transpose(x, 1, -1)
+        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+
+        x = self.prenet(x, x_mask)
+        if self.n_spks > 1:
+            x = torch.cat([x, spks.unsqueeze(-1).repeat(1, 1, x.shape[-1])], dim=1)
+        x = self.encoder(x, x_mask)
+        mu = self.proj_m(x) * x_mask
+
+        x_dp = torch.detach(x)
+        logw = self.proj_w(x_dp, x_mask)
+
+        return mu, logw, x_mask

chatterbox/models/s3gen/matcha/transformer.py

@@ -0,0 +1,316 @@
+from typing import Any, Dict, Optional
+
+import torch
+import torch.nn as nn
+from diffusers.models.attention import (
+    GEGLU,
+    GELU,
+    AdaLayerNorm,
+    AdaLayerNormZero,
+    ApproximateGELU,
+)
+from diffusers.models.attention_processor import Attention
+from diffusers.models.lora import LoRACompatibleLinear
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+
+
+class SnakeBeta(nn.Module):
+    """
+    A modified Snake function which uses separate parameters for the magnitude of the periodic components
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter that controls frequency
+        - beta - trainable parameter that controls magnitude
+    References:
+        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snakebeta(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    """
+
+    def __init__(self, in_features, out_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):
+        """
+        Initialization.
+        INPUT:
+            - in_features: shape of the input
+            - alpha - trainable parameter that controls frequency
+            - beta - trainable parameter that controls magnitude
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            beta is initialized to 1 by default, higher values = higher-magnitude.
+            alpha will be trained along with the rest of your model.
+        """
+        super().__init__()
+        self.in_features = out_features if isinstance(out_features, list) else [out_features]
+        self.proj = LoRACompatibleLinear(in_features, out_features)
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale:  # log scale alphas initialized to zeros
+            self.alpha = nn.Parameter(torch.zeros(self.in_features) * alpha)
+            self.beta = nn.Parameter(torch.zeros(self.in_features) * alpha)
+        else:  # linear scale alphas initialized to ones
+            self.alpha = nn.Parameter(torch.ones(self.in_features) * alpha)
+            self.beta = nn.Parameter(torch.ones(self.in_features) * alpha)
+
+        self.alpha.requires_grad = alpha_trainable
+        self.beta.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        """
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        SnakeBeta ∶= x + 1/b * sin^2 (xa)
+        """
+        x = self.proj(x)
+        if self.alpha_logscale:
+            alpha = torch.exp(self.alpha)
+            beta = torch.exp(self.beta)
+        else:
+            alpha = self.alpha
+            beta = self.beta
+
+        x = x + (1.0 / (beta + self.no_div_by_zero)) * torch.pow(torch.sin(x * alpha), 2)
+
+        return x
+
+
+class FeedForward(nn.Module):
+    r"""
+    A feed-forward layer.
+
+    Parameters:
+        dim (`int`): The number of channels in the input.
+        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
+        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        mult: int = 4,
+        dropout: float = 0.0,
+        activation_fn: str = "geglu",
+        final_dropout: bool = False,
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+
+        if activation_fn == "gelu":
+            act_fn = GELU(dim, inner_dim)
+        if activation_fn == "gelu-approximate":
+            act_fn = GELU(dim, inner_dim, approximate="tanh")
+        elif activation_fn == "geglu":
+            act_fn = GEGLU(dim, inner_dim)
+        elif activation_fn == "geglu-approximate":
+            act_fn = ApproximateGELU(dim, inner_dim)
+        elif activation_fn == "snakebeta":
+            act_fn = SnakeBeta(dim, inner_dim)
+
+        self.net = nn.ModuleList([])
+        # project in
+        self.net.append(act_fn)
+        # project dropout
+        self.net.append(nn.Dropout(dropout))
+        # project out
+        self.net.append(LoRACompatibleLinear(inner_dim, dim_out))
+        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
+        if final_dropout:
+            self.net.append(nn.Dropout(dropout))
+
+    def forward(self, hidden_states):
+        for module in self.net:
+            hidden_states = module(hidden_states)
+        return hidden_states
+
+
+@maybe_allow_in_graph
+class BasicTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm (:
+            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        double_self_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_elementwise_affine: bool = True,
+        norm_type: str = "layer_norm",
+        final_dropout: bool = False,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+
+        self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
+        self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
+
+        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
+            raise ValueError(
+                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
+                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
+            )
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        if self.use_ada_layer_norm:
+            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
+        elif self.use_ada_layer_norm_zero:
+            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
+        else:
+            self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+        )
+
+        # 2. Cross-Attn
+        if cross_attention_dim is not None or double_self_attention:
+            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
+            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
+            # the second cross attention block.
+            self.norm2 = (
+                AdaLayerNorm(dim, num_embeds_ada_norm)
+                if self.use_ada_layer_norm
+                else nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+            )
+            self.attn2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim if not double_self_attention else None,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+                # scale_qk=False, # uncomment this to not to use flash attention
+            )  # is self-attn if encoder_hidden_states is none
+        else:
+            self.norm2 = None
+            self.attn2 = None
+
+        # 3. Feed-forward
+        self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+    ):
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 1. Self-Attention
+        if self.use_ada_layer_norm:
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.use_ada_layer_norm_zero:
+            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        else:
+            norm_hidden_states = self.norm1(hidden_states)
+
+        cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+            attention_mask=encoder_attention_mask if self.only_cross_attention else attention_mask,
+            **cross_attention_kwargs,
+        )
+        if self.use_ada_layer_norm_zero:
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        hidden_states = attn_output + hidden_states
+
+        # 2. Cross-Attention
+        if self.attn2 is not None:
+            norm_hidden_states = (
+                self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+            )
+
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+            hidden_states = attn_output + hidden_states
+
+        # 3. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+
+        if self.use_ada_layer_norm_zero:
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+                raise ValueError(
+                    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+                )
+
+            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+            ff_output = torch.cat(
+                [self.ff(hid_slice) for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)],
+                dim=self._chunk_dim,
+            )
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        if self.use_ada_layer_norm_zero:
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+        hidden_states = ff_output + hidden_states
+
+        return hidden_states

chatterbox/models/s3gen/s3gen.py

@@ -0,0 +1,298 @@
+# Modified from CosyVoice https://github.com/FunAudioLLM/CosyVoice
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+
+import numpy as np
+import torch
+import torchaudio as ta
+from functools import lru_cache
+from typing import Optional
+
+from ..s3tokenizer import S3_SR, SPEECH_VOCAB_SIZE, S3Tokenizer
+from .const import S3GEN_SR
+from .flow import CausalMaskedDiffWithXvec
+from .xvector import CAMPPlus
+from .utils.mel import mel_spectrogram
+from .f0_predictor import ConvRNNF0Predictor
+from .hifigan import HiFTGenerator
+from .transformer.upsample_encoder import UpsampleConformerEncoder
+from .flow_matching import CausalConditionalCFM
+from .decoder import ConditionalDecoder
+from .configs import CFM_PARAMS
+
+
+def drop_invalid_tokens(x):
+    assert len(x.shape) <= 2 and x.shape[0] == 1, "only batch size of one allowed for now"
+    return x[x < SPEECH_VOCAB_SIZE]
+
+
+# TODO: global resampler cache
+@lru_cache(100)
+def get_resampler(src_sr, dst_sr, device):
+    return ta.transforms.Resample(src_sr, dst_sr).to(device)
+
+
+class S3Token2Mel(torch.nn.Module):
+    """
+    CosyVoice2's CFM decoder maps S3 speech tokens to mel-spectrograms.
+
+    TODO: make these modules configurable?
+    """
+    def __init__(self):
+        super().__init__()
+        self.tokenizer = S3Tokenizer("speech_tokenizer_v2_25hz")
+        self.mel_extractor = mel_spectrogram # TODO: make it a torch module?
+        self.speaker_encoder = CAMPPlus()  # use default args
+
+        encoder = UpsampleConformerEncoder(
+            output_size=512,
+            attention_heads=8,
+            linear_units=2048,
+            num_blocks=6,
+            dropout_rate=0.1,
+            positional_dropout_rate=0.1,
+            attention_dropout_rate=0.1,
+            normalize_before=True,
+            input_layer='linear',
+            pos_enc_layer_type='rel_pos_espnet',
+            selfattention_layer_type='rel_selfattn',
+            input_size=512,
+            use_cnn_module=False,
+            macaron_style=False,
+        )
+
+        estimator = ConditionalDecoder(
+            in_channels=320,
+            out_channels=80,
+            causal=True,
+            channels=[256],
+            dropout=0.0,
+            attention_head_dim=64,
+            n_blocks=4,
+            num_mid_blocks=12,
+            num_heads=8,
+            act_fn='gelu',
+        )
+        cfm_params = CFM_PARAMS
+        decoder = CausalConditionalCFM(
+            spk_emb_dim=80,
+            cfm_params=cfm_params,
+            estimator=estimator,
+        )
+
+        self.flow = CausalMaskedDiffWithXvec(
+            encoder=encoder,
+            decoder=decoder
+        )
+
+        self.resamplers = {}
+
+    @property
+    def device(self):
+        params = self.tokenizer.parameters()
+        return next(params).device
+
+    def embed_ref(
+        self,
+        ref_wav: torch.Tensor,
+        ref_sr: int,
+        device="auto",
+        ref_fade_out=True,
+    ):
+        device = self.device if device == "auto" else device
+        if isinstance(ref_wav, np.ndarray):
+            ref_wav = torch.from_numpy(ref_wav).float()
+
+        if ref_wav.device != device:
+            ref_wav = ref_wav.to(device)
+
+        if len(ref_wav.shape) == 1:
+            ref_wav = ref_wav.unsqueeze(0)  # (B, L)
+
+        if ref_wav.size(1) > 10 * ref_sr:
+            print("WARNING: cosydec received ref longer than 10s")
+
+        ref_wav_24 = ref_wav
+        if ref_sr != S3GEN_SR:
+            ref_wav_24 = get_resampler(ref_sr, S3GEN_SR, device)(ref_wav)
+
+        ref_mels_24 = self.mel_extractor(ref_wav_24).transpose(1, 2).to(device)
+        ref_mels_24_len = None
+
+        # Resample to 16kHz
+        ref_wav_16 = get_resampler(ref_sr, S3_SR, device)(ref_wav).to(device)
+
+        # Speaker embedding
+        ref_x_vector = self.speaker_encoder.inference(ref_wav_16)
+
+        # Tokenize 16khz reference
+        ref_speech_tokens, ref_speech_token_lens = self.tokenizer(ref_wav_16)
+
+        # Make sure mel_len = 2 * stoken_len (happens when the input is not padded to multiple of 40ms)
+        if ref_mels_24.shape[1] != 2 * ref_speech_tokens.shape[1]:
+            logging.warning(
+                "Reference mel length is not equal to 2 * reference token length.\n"
+            )
+            ref_speech_tokens = ref_speech_tokens[:, :ref_mels_24.shape[1] // 2]
+            ref_speech_token_lens[0] = ref_speech_tokens.shape[1]
+
+        return dict(
+            prompt_token=ref_speech_tokens.to(device),
+            prompt_token_len=ref_speech_token_lens,
+            prompt_feat=ref_mels_24,
+            prompt_feat_len=ref_mels_24_len,
+            embedding=ref_x_vector,
+        )
+
+    def forward(
+        self,
+        speech_tokens: torch.LongTensor,
+        # locally-computed ref embedding (mutex with ref_dict)
+        ref_wav: Optional[torch.Tensor],
+        ref_sr: Optional[int],
+        # pre-computed ref embedding (prod API)
+        ref_dict: Optional[dict] = None,
+        finalize: bool = False,
+    ):
+        """
+        Generate waveforms from S3 speech tokens and a reference waveform, which the speaker timbre is inferred from.
+
+        NOTE:
+        - The speaker encoder accepts 16 kHz waveform.
+        - S3TokenizerV2 accepts 16 kHz waveform.
+        - The mel-spectrogram for the reference assumes 24 kHz input signal.
+        - This function is designed for batch_size=1 only.
+
+        Args
+        ----
+        - `speech_tokens`: S3 speech tokens [B=1, T]
+        - `ref_wav`: reference waveform (`torch.Tensor` with shape=[B=1, T])
+        - `ref_sr`: reference sample rate
+        - `finalize`: whether streaming is finished or not. Note that if False, the last 3 tokens will be ignored.
+        """
+        assert (ref_wav is None) ^ (ref_dict is None), f"Must provide exactly one of ref_wav or ref_dict (got {ref_wav} and {ref_dict})"
+
+        if ref_dict is None:
+            ref_dict = self.embed_ref(ref_wav, ref_sr)
+        else:
+            # type/device casting (all values will be numpy if it's from a prod API call)
+            for rk in list(ref_dict):
+                if isinstance(ref_dict[rk], np.ndarray):
+                    ref_dict[rk] = torch.from_numpy(ref_dict[rk])
+                if torch.is_tensor(ref_dict[rk]):
+                    ref_dict[rk] = ref_dict[rk].to(self.device)
+
+        if len(speech_tokens.shape) == 1:
+            speech_tokens = speech_tokens.unsqueeze(0)
+
+        # assert speech_tokens.shape[0] == 1, "only batch size of one allowed for now"
+        speech_token_lens = torch.LongTensor([speech_tokens.size(1)]).to(self.device)
+
+        output_mels, _ = self.flow.inference(
+            token=speech_tokens,
+            token_len=speech_token_lens,
+            finalize=finalize,
+            **ref_dict,
+        )
+        return output_mels
+
+
+class S3Token2Wav(S3Token2Mel):
+    """
+    The decoder of CosyVoice2 is a concat of token-to-mel (CFM) and a mel-to-waveform (HiFiGAN) modules.
+
+    TODO: make these modules configurable?
+    """
+
+    def __init__(self):
+        super().__init__()
+
+        f0_predictor = ConvRNNF0Predictor()
+        self.mel2wav = HiFTGenerator(
+            sampling_rate=S3GEN_SR,
+            upsample_rates=[8, 5, 3],
+            upsample_kernel_sizes=[16, 11, 7],
+            source_resblock_kernel_sizes=[7, 7, 11],
+            source_resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+            f0_predictor=f0_predictor,
+        )
+
+        # silence out a few ms and fade audio in to reduce artifacts
+        n_trim = S3GEN_SR // 50  # 20ms = half of a frame
+        trim_fade = torch.zeros(2 * n_trim)
+        trim_fade[n_trim:] = (torch.cos(torch.linspace(torch.pi, 0, n_trim)) + 1) / 2
+        self.register_buffer("trim_fade", trim_fade, persistent=False) # (buffers get automatic device casting)
+
+    def forward(
+        self,
+        speech_tokens,
+        # locally-computed ref embedding (mutex with ref_dict)
+        ref_wav: Optional[torch.Tensor],
+        ref_sr: Optional[int],
+        # pre-computed ref embedding (prod API)
+        ref_dict: Optional[dict] = None,
+        finalize: bool = False
+    ):
+        output_mels = super().forward(speech_tokens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize)
+
+        # TODO jrm: ignoring the speed control (mel interpolation) and the HiFTGAN caching mechanisms for now.
+        hift_cache_source = torch.zeros(1, 1, 0).to(self.device)
+
+        output_wavs, *_ = self.mel2wav.inference(speech_feat=output_mels, cache_source=hift_cache_source)
+
+        if not self.training:
+            # NOTE: ad-hoc method to reduce "spillover" from the reference clip.
+            output_wavs[:, :len(self.trim_fade)] *= self.trim_fade
+
+        return output_wavs
+
+    @torch.inference_mode()
+    def flow_inference(
+        self,
+        speech_tokens,
+        # locally-computed ref embedding (mutex with ref_dict)
+        ref_wav: Optional[torch.Tensor] = None,
+        ref_sr: Optional[int] = None,
+        # pre-computed ref embedding (prod API)
+        ref_dict: Optional[dict] = None,
+        finalize: bool = False,
+    ):
+        return super().forward(speech_tokens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize)
+
+    @torch.inference_mode()
+    def hift_inference(self, speech_feat, cache_source: torch.Tensor = None):
+        if cache_source is None:
+            cache_source = torch.zeros(1, 1, 0).to(self.device)
+        return self.mel2wav.inference(speech_feat=speech_feat, cache_source=cache_source)
+
+    @torch.inference_mode()
+    def inference(
+        self,
+        speech_tokens,
+        # locally-computed ref embedding (mutex with ref_dict)
+        ref_wav: Optional[torch.Tensor] = None,
+        ref_sr: Optional[int] = None,
+        # pre-computed ref embedding (prod API)
+        ref_dict: Optional[dict] = None,
+        cache_source: torch.Tensor = None, # NOTE: this arg is for streaming, it can probably be removed here
+        finalize: bool = True,
+    ):
+        output_mels = self.flow_inference(speech_tokens, ref_wav=ref_wav, ref_sr=ref_sr, ref_dict=ref_dict, finalize=finalize)
+        output_wavs, output_sources = self.hift_inference(output_mels, cache_source)
+
+        # NOTE: ad-hoc method to reduce "spillover" from the reference clip.
+        output_wavs[:, :len(self.trim_fade)] *= self.trim_fade
+
+        return output_wavs, output_sources

chatterbox/models/s3gen/transformer/activation.py

@@ -0,0 +1,84 @@
+# Copyright (c) 2020 Johns Hopkins University (Shinji Watanabe)
+#               2020 Northwestern Polytechnical University (Pengcheng Guo)
+#               2020 Mobvoi Inc (Binbin Zhang)
+#               2024 Alibaba Inc (Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Swish() activation function for Conformer."""
+
+import torch
+from torch import nn, sin, pow
+from torch.nn import Parameter
+
+
+class Swish(torch.nn.Module):
+    """Construct an Swish object."""
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Return Swish activation function."""
+        return x * torch.sigmoid(x)
+
+
+# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
+#   LICENSE is in incl_licenses directory.
+class Snake(nn.Module):
+    '''
+    Implementation of a sine-based periodic activation function
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter
+    References:
+        - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snake(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    '''
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+        '''
+        Initialization.
+        INPUT:
+            - in_features: shape of the input
+            - alpha: trainable parameter
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            alpha will be trained along with the rest of your model.
+        '''
+        super(Snake, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale:  # log scale alphas initialized to zeros
+            self.alpha = Parameter(torch.zeros(in_features) * alpha)
+        else:  # linear scale alphas initialized to ones
+            self.alpha = Parameter(torch.ones(in_features) * alpha)
+
+        self.alpha.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        '''
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        Snake ∶= x + 1/a * sin^2 (xa)
+        '''
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # line up with x to [B, C, T]
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+        x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x

chatterbox/models/s3gen/transformer/attention.py

@@ -0,0 +1,330 @@
+# Copyright (c) 2019 Shigeki Karita
+#               2020 Mobvoi Inc (Binbin Zhang)
+#               2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
+#               2024 Alibaba Inc (Xiang Lyu)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Multi-Head Attention layer definition."""
+
+import math
+from typing import Tuple
+
+import torch
+from torch import nn
+
+
+class MultiHeadedAttention(nn.Module):
+    """Multi-Head Attention layer.
+
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+
+    """
+
+    def __init__(self,
+                 n_head: int,
+                 n_feat: int,
+                 dropout_rate: float,
+                 key_bias: bool = True):
+        """Construct an MultiHeadedAttention object."""
+        super().__init__()
+        assert n_feat % n_head == 0
+        # We assume d_v always equals d_k
+        self.d_k = n_feat // n_head
+        self.h = n_head
+        self.linear_q = nn.Linear(n_feat, n_feat)
+        self.linear_k = nn.Linear(n_feat, n_feat, bias=key_bias)
+        self.linear_v = nn.Linear(n_feat, n_feat)
+        self.linear_out = nn.Linear(n_feat, n_feat)
+        self.dropout = nn.Dropout(p=dropout_rate)
+
+    def forward_qkv(
+        self, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Transform query, key and value.
+
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+
+        Returns:
+            torch.Tensor: Transformed query tensor, size
+                (#batch, n_head, time1, d_k).
+            torch.Tensor: Transformed key tensor, size
+                (#batch, n_head, time2, d_k).
+            torch.Tensor: Transformed value tensor, size
+                (#batch, n_head, time2, d_k).
+
+        """
+        n_batch = query.size(0)
+        q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
+        k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
+        v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
+        q = q.transpose(1, 2)  # (batch, head, time1, d_k)
+        k = k.transpose(1, 2)  # (batch, head, time2, d_k)
+        v = v.transpose(1, 2)  # (batch, head, time2, d_k)
+
+        return q, k, v
+
+    def forward_attention(
+        self,
+        value: torch.Tensor,
+        scores: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool)
+    ) -> torch.Tensor:
+        """Compute attention context vector.
+
+        Args:
+            value (torch.Tensor): Transformed value, size
+                (#batch, n_head, time2, d_k).
+            scores (torch.Tensor): Attention score, size
+                (#batch, n_head, time1, time2).
+            mask (torch.Tensor): Mask, size (#batch, 1, time2) or
+                (#batch, time1, time2), (0, 0, 0) means fake mask.
+
+        Returns:
+            torch.Tensor: Transformed value (#batch, time1, d_model)
+                weighted by the attention score (#batch, time1, time2).
+
+        """
+        n_batch = value.size(0)
+        # NOTE(xcsong): When will `if mask.size(2) > 0` be True?
+        #   1. onnx(16/4) [WHY? Because we feed real cache & real mask for the
+        #           1st chunk to ease the onnx export.]
+        #   2. pytorch training
+        if mask.size(2) > 0:  # time2 > 0
+            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
+            # For last chunk, time2 might be larger than scores.size(-1)
+            mask = mask[:, :, :, :scores.size(-1)]  # (batch, 1, *, time2)
+            scores = scores.masked_fill(mask, -float('inf'))
+            attn = torch.softmax(scores, dim=-1).masked_fill(
+                mask, 0.0)  # (batch, head, time1, time2)
+        # NOTE(xcsong): When will `if mask.size(2) > 0` be False?
+        #   1. onnx(16/-1, -1/-1, 16/0)
+        #   2. jit (16/-1, -1/-1, 16/0, 16/4)
+        else:
+            attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
+
+        p_attn = self.dropout(attn)
+        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
+        x = (x.transpose(1, 2).contiguous().view(n_batch, -1,
+                                                 self.h * self.d_k)
+             )  # (batch, time1, d_model)
+
+        return self.linear_out(x)  # (batch, time1, d_model)
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        pos_emb: torch.Tensor = torch.empty(0),
+        cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute scaled dot product attention.
+
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2).
+                1.When applying cross attention between decoder and encoder,
+                the batch padding mask for input is in (#batch, 1, T) shape.
+                2.When applying self attention of encoder,
+                the mask is in (#batch, T, T)  shape.
+                3.When applying self attention of decoder,
+                the mask is in (#batch, L, L)  shape.
+                4.If the different position in decoder see different block
+                of the encoder, such as Mocha, the passed in mask could be
+                in (#batch, L, T) shape. But there is no such case in current
+                CosyVoice.
+            cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+
+
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+            torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+
+        """
+        q, k, v = self.forward_qkv(query, key, value)
+
+        # NOTE(xcsong):
+        #   when export onnx model, for 1st chunk, we feed
+        #       cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
+        #       or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
+        #       In all modes, `if cache.size(0) > 0` will alwayse be `True`
+        #       and we will always do splitting and
+        #       concatnation(this will simplify onnx export). Note that
+        #       it's OK to concat & split zero-shaped tensors(see code below).
+        #   when export jit  model, for 1st chunk, we always feed
+        #       cache(0, 0, 0, 0) since jit supports dynamic if-branch.
+        # >>> a = torch.ones((1, 2, 0, 4))
+        # >>> b = torch.ones((1, 2, 3, 4))
+        # >>> c = torch.cat((a, b), dim=2)
+        # >>> torch.equal(b, c)        # True
+        # >>> d = torch.split(a, 2, dim=-1)
+        # >>> torch.equal(d[0], d[1])  # True
+        if cache.size(0) > 0:
+            key_cache, value_cache = torch.split(cache,
+                                                 cache.size(-1) // 2,
+                                                 dim=-1)
+            k = torch.cat([key_cache, k], dim=2)
+            v = torch.cat([value_cache, v], dim=2)
+        # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
+        #   non-trivial to calculate `next_cache_start` here.
+        new_cache = torch.cat((k, v), dim=-1)
+
+        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
+        return self.forward_attention(v, scores, mask), new_cache
+
+
+class RelPositionMultiHeadedAttention(MultiHeadedAttention):
+    """Multi-Head Attention layer with relative position encoding.
+    Paper: https://arxiv.org/abs/1901.02860
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+    """
+
+    def __init__(self,
+                 n_head: int,
+                 n_feat: int,
+                 dropout_rate: float,
+                 key_bias: bool = True):
+        """Construct an RelPositionMultiHeadedAttention object."""
+        super().__init__(n_head, n_feat, dropout_rate, key_bias)
+        # linear transformation for positional encoding
+        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
+        # these two learnable bias are used in matrix c and matrix d
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
+        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
+        torch.nn.init.xavier_uniform_(self.pos_bias_u)
+        torch.nn.init.xavier_uniform_(self.pos_bias_v)
+
+    def rel_shift(self, x: torch.Tensor) -> torch.Tensor:
+        """Compute relative positional encoding.
+
+        Args:
+            x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1).
+            time1 means the length of query vector.
+
+        Returns:
+            torch.Tensor: Output tensor.
+
+        """
+        zero_pad = torch.zeros((x.size()[0], x.size()[1], x.size()[2], 1),
+                               device=x.device,
+                               dtype=x.dtype)
+        x_padded = torch.cat([zero_pad, x], dim=-1)
+
+        x_padded = x_padded.view(x.size()[0],
+                                 x.size()[1],
+                                 x.size(3) + 1, x.size(2))
+        x = x_padded[:, :, 1:].view_as(x)[
+            :, :, :, : x.size(-1) // 2 + 1
+        ]  # only keep the positions from 0 to time2
+        return x
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        mask: torch.Tensor = torch.ones((0, 0, 0), dtype=torch.bool),
+        pos_emb: torch.Tensor = torch.empty(0),
+        cache: torch.Tensor = torch.zeros((0, 0, 0, 0))
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2), (0, 0, 0) means fake mask.
+            pos_emb (torch.Tensor): Positional embedding tensor
+                (#batch, time2, size).
+            cache (torch.Tensor): Cache tensor (1, head, cache_t, d_k * 2),
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+            torch.Tensor: Cache tensor (1, head, cache_t + time1, d_k * 2)
+                where `cache_t == chunk_size * num_decoding_left_chunks`
+                and `head * d_k == size`
+        """
+        q, k, v = self.forward_qkv(query, key, value)
+        q = q.transpose(1, 2)  # (batch, time1, head, d_k)
+
+        # NOTE(xcsong):
+        #   when export onnx model, for 1st chunk, we feed
+        #       cache(1, head, 0, d_k * 2) (16/-1, -1/-1, 16/0 mode)
+        #       or cache(1, head, real_cache_t, d_k * 2) (16/4 mode).
+        #       In all modes, `if cache.size(0) > 0` will alwayse be `True`
+        #       and we will always do splitting and
+        #       concatnation(this will simplify onnx export). Note that
+        #       it's OK to concat & split zero-shaped tensors(see code below).
+        #   when export jit  model, for 1st chunk, we always feed
+        #       cache(0, 0, 0, 0) since jit supports dynamic if-branch.
+        # >>> a = torch.ones((1, 2, 0, 4))
+        # >>> b = torch.ones((1, 2, 3, 4))
+        # >>> c = torch.cat((a, b), dim=2)
+        # >>> torch.equal(b, c)        # True
+        # >>> d = torch.split(a, 2, dim=-1)
+        # >>> torch.equal(d[0], d[1])  # True
+        if cache.size(0) > 0:
+            key_cache, value_cache = torch.split(cache,
+                                                 cache.size(-1) // 2,
+                                                 dim=-1)
+            k = torch.cat([key_cache, k], dim=2)
+            v = torch.cat([value_cache, v], dim=2)
+        # NOTE(xcsong): We do cache slicing in encoder.forward_chunk, since it's
+        #   non-trivial to calculate `next_cache_start` here.
+        new_cache = torch.cat((k, v), dim=-1)
+
+        n_batch_pos = pos_emb.size(0)
+        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
+        p = p.transpose(1, 2)  # (batch, head, time1, d_k)
+
+        # (batch, head, time1, d_k)
+        q_with_bias_u = (q + self.pos_bias_u.to(q.device)).transpose(1, 2)
+        # (batch, head, time1, d_k)
+        q_with_bias_v = (q + self.pos_bias_v.to(q.device)).transpose(1, 2)
+
+        # compute attention score
+        # first compute matrix a and matrix c
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        # (batch, head, time1, time2)
+        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
+
+        # compute matrix b and matrix d
+        # (batch, head, time1, time2)
+        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+        # NOTE(Xiang Lyu): Keep rel_shift since espnet rel_pos_emb is used
+        if matrix_ac.shape != matrix_bd.shape:
+            matrix_bd = self.rel_shift(matrix_bd)
+
+        scores = (matrix_ac + matrix_bd) / math.sqrt(
+            self.d_k)  # (batch, head, time1, time2)
+
+        return self.forward_attention(v, scores, mask), new_cache