[model] support FireRedLID (#39290)

Signed-off-by: PatchouliTaisa <patchychen@tencent.com>
Co-authored-by: PatchouliTaisa <patchychen@tencent.com>

docs/models/supported_models.md

@@ -661,6 +661,7 @@ Speech2Text models trained specifically for Automatic Speech Recognition.
 | ------------ | ------ | ----------------- | -------------------- | ------------------------- |
 | `CohereAsrForConditionalGeneration` | Cohere-Transcribe | `CohereLabs/cohere-transcribe-03-2026` | | |
 | `FireRedASR2ForConditionalGeneration` | FireRedASR2 | `allendou/FireRedASR2-LLM-vllm`, etc. | | |
+| `FireRedLIDForConditionalGeneration` | FireRedLID | `PatchyTisa/FireRedLID-vllm`, etc. | | |
 | `FunASRForConditionalGeneration` | FunASR | `allendou/Fun-ASR-Nano-2512-vllm`, etc. | | |
 | `Gemma3nForConditionalGeneration` | Gemma3n | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | |
 | `GlmAsrForConditionalGeneration` | GLM-ASR | `zai-org/GLM-ASR-Nano-2512` | ✅︎ | ✅︎ |

examples/offline_inference/audio_language.py

@@ -537,9 +537,30 @@ def run_whisper(question: str, audio_count: int) -> ModelRequestData:
     )
 
 
+# FireRedLID
+def run_fireredlid(question: str, audio_count: int) -> ModelRequestData:
+    assert audio_count == 1, "FireRedLID only supports single audio input per prompt"
+    model_name = "PatchyTisa/FireRedLID-vllm"
+
+    prompt = "<sos>"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=8,
+        max_num_seqs=5,
+        limit_mm_per_prompt={"audio": audio_count},
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+    )
+
+
 model_example_map = {
     "audioflamingo3": run_audioflamingo3,
     "cohere_asr": run_cohere_asr,
+    "fireredlid": run_fireredlid,
     "funaudiochat": run_funaudiochat,
     "gemma3n": run_gemma3n,
     "glmasr": run_glmasr,

examples/offline_inference/encoder_decoder_multimodal.py

@@ -55,7 +55,91 @@ def run_whisper():
     )
 
 
+def run_fireredasr2():
+    """
+    FireRedASR2 – Automatic Speech Recognition model.
+
+    This model uses a Conformer encoder + Qwen2 LLM decoder architecture
+    for speech-to-text transcription.  Audio is passed via the implicit
+    prompt format with the ``<|AUDIO|>`` placeholder token.
+    """
+    engine_args = EngineArgs(
+        model="allendou/FireRedASR2-LLM-vllm",
+        max_model_len=448,
+        max_num_seqs=16,
+        limit_mm_per_prompt={"audio": 1},
+    )
+
+    prompt_str = (
+        "<|im_start|>user\n<|AUDIO|>请转写音频为文字<|im_end|>\n<|im_start|>assistant\n"
+    )
+
+    prompts = [
+        {  # Implicit prompt with audio
+            "prompt": prompt_str,
+            "multi_modal_data": {
+                "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
+            },
+        },
+        {  # Another audio sample
+            "prompt": prompt_str,
+            "multi_modal_data": {
+                "audio": AudioAsset("winning_call").audio_and_sample_rate,
+            },
+        },
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
+def run_fireredlid():
+    """
+    FireRedLID – Language Identification model.
+
+    This encoder-decoder model identifies the spoken language of an audio
+    clip. It outputs at most 2 tokens representing the detected language
+    (e.g. "en", "zh mandarin").
+    """
+    engine_args = EngineArgs(
+        model="PatchyTisa/FireRedLID-vllm",
+        max_model_len=8,
+        max_num_seqs=16,
+        limit_mm_per_prompt={"audio": 1},
+    )
+
+    prompts = [
+        {  # Test explicit encoder/decoder prompt
+            "encoder_prompt": {
+                "prompt": "",
+                "multi_modal_data": {
+                    "audio": AudioAsset("mary_had_lamb").audio_and_sample_rate,
+                },
+            },
+            "decoder_prompt": "<sos>",
+        },
+        {  # Another audio sample
+            "encoder_prompt": {
+                "prompt": "",
+                "multi_modal_data": {
+                    "audio": AudioAsset("winning_call").audio_and_sample_rate,
+                },
+            },
+            "decoder_prompt": "<sos>",
+        },
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 model_example_map = {
+    "fireredasr2": run_fireredasr2,
+    "fireredlid": run_fireredlid,
     "whisper": run_whisper,
 }
 

examples/online_serving/openai_lid_client.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Language Identification (LID) demo using the FireRedLID model on vLLM.
+
+FireRedLID is an audio encoder-decoder model that identifies the spoken
+language of an audio clip. Unlike ASR models that output full transcriptions,
+FireRedLID outputs at most 2 tokens representing the detected language
+(e.g. "en", "zh mandarin").
+
+Start the vLLM server:
+
+    vllm serve PatchyTisa/FireRedLID-vllm
+
+Then run this script:
+
+    # Use the built-in sample audio
+    python examples/online_serving/openai_lid_client.py
+
+    # Use your own audio file(s)
+    python examples/online_serving/openai_lid_client.py \
+        --audio_paths audio_en.wav audio_zh.wav audio_fr.wav
+
+    # Batch-identify multiple files in one run
+    python examples/online_serving/openai_lid_client.py \
+        --audio_paths /path/to/dir/*.wav
+
+Requirements:
+- vLLM with audio support
+- openai Python SDK
+- kaldi_native_fbank (pulled in by the model)
+"""
+
+import argparse
+import json
+import os
+
+from openai import OpenAI
+
+from vllm.assets.audio import AudioAsset
+
+# ──────────────────────────────────────────────────────────────────────
+# Helpers
+# ──────────────────────────────────────────────────────────────────────
+
+
+def identify_language(
+    audio_path: str,
+    client: OpenAI,
+    model: str,
+) -> str:
+    """
+    Send a single audio file to the vLLM transcription endpoint and return
+    the detected language tag.
+
+    FireRedLID re-uses the OpenAI-compatible ``/v1/audio/transcriptions``
+    endpoint. The "transcription" it returns is actually the language label
+    (e.g. ``"en"`` or ``"zh mandarin"``).
+    """
+    with open(audio_path, "rb") as f:
+        result = client.audio.transcriptions.create(
+            file=f,
+            model=model,
+            response_format="json",
+            temperature=0.0,
+        )
+    return result.text.strip()
+
+
+def identify_language_raw(
+    audio_path: str,
+    model: str,
+    api_base: str,
+) -> str:
+    """
+    Same as :func:`identify_language` but uses raw HTTP so that the demo
+    works without the ``openai`` SDK (useful for quick debugging).
+    """
+    import requests
+
+    url = f"{api_base}/audio/transcriptions"
+    with open(audio_path, "rb") as f:
+        files = {"file": (os.path.basename(audio_path), f)}
+        data = {
+            "model": model,
+            "response_format": "json",
+        }
+        resp = requests.post(url, files=files, data=data)
+        resp.raise_for_status()
+    return resp.json()["text"].strip()
+
+
+def identify_language_streaming(
+    audio_path: str,
+    model: str,
+    api_base: str,
+) -> str:
+    """
+    Streaming variant – demonstrates the streaming transcription endpoint.
+    For a 1-2 token output the stream finishes almost instantly, but this
+    shows that the API path works end-to-end.
+    """
+    import requests
+
+    url = f"{api_base}/audio/transcriptions"
+    with open(audio_path, "rb") as f:
+        files = {"file": (os.path.basename(audio_path), f)}
+        data = {
+            "stream": "true",
+            "model": model,
+            "response_format": "json",
+        }
+        response = requests.post(url, files=files, data=data, stream=True)
+        response.raise_for_status()
+
+        tokens: list[str] = []
+        for chunk in response.iter_lines(
+            chunk_size=8192, decode_unicode=False, delimiter=b"\n"
+        ):
+            if not chunk:
+                continue
+            payload = json.loads(chunk[len("data: ") :].decode("utf-8"))
+            choice = payload["choices"][0]
+            delta = choice.get("delta", {}).get("content", "")
+            if delta:
+                tokens.append(delta)
+            if choice.get("finish_reason") is not None:
+                break
+
+    return "".join(tokens).strip()
+
+
+# ──────────────────────────────────────────────────────────────────────
+# Main
+# ──────────────────────────────────────────────────────────────────────
+
+
+def main(args: argparse.Namespace) -> None:
+    api_base = args.api_base.rstrip("/")
+    client = OpenAI(api_key="EMPTY", base_url=api_base)
+    model = client.models.list().data[0].id
+    print(f"Model : {model}")
+    print(f"Server: {api_base}\n")
+
+    # Resolve audio paths ------------------------------------------------
+    if args.audio_paths:
+        audio_paths = args.audio_paths
+    else:
+        # Fall back to the built-in vLLM sample audios (both are English).
+        audio_paths = [
+            str(AudioAsset("mary_had_lamb").get_local_path()),
+            str(AudioAsset("winning_call").get_local_path()),
+        ]
+
+    # Run LID for each file ----------------------------------------------
+    print(f"{'Audio File':<50} {'Language (sync)':<20} {'Language (stream)'}")
+    print("-" * 90)
+
+    for path in audio_paths:
+        basename = os.path.basename(path)
+
+        # 1) Synchronous via OpenAI SDK
+        lang_sync = identify_language(path, client, model)
+
+        # 2) Streaming via raw HTTP
+        lang_stream = identify_language_streaming(path, model, api_base)
+
+        print(f"{basename:<50} {lang_sync:<20} {lang_stream}")
+
+    print()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="FireRedLID – Language Identification demo via vLLM",
+    )
+    parser.add_argument(
+        "--audio_paths",
+        nargs="+",
+        default=None,
+        help=(
+            "One or more audio files to identify. "
+            "If omitted, uses vLLM's built-in sample audios."
+        ),
+    )
+    parser.add_argument(
+        "--api_base",
+        type=str,
+        default="http://localhost:8000/v1",
+        help="vLLM API base URL (default: http://localhost:8000/v1)",
+    )
+    args = parser.parse_args()
+    main(args)

tests/models/registry.py

@@ -820,6 +820,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "FireRedASR2ForConditionalGeneration": _HfExamplesInfo(
         "allendou/FireRedASR2-LLM-vllm",
     ),
+    "FireRedLIDForConditionalGeneration": _HfExamplesInfo(
+        "PatchyTisa/FireRedLID-vllm",
+    ),
     "FunASRForConditionalGeneration": _HfExamplesInfo(
         "allendou/Fun-ASR-Nano-2512-vllm",
     ),

vllm/model_executor/models/conformer_encoder.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Shared Conformer encoder components for FireRedASR2 and FireRedLID.
+
+Both models use the same Conformer-based audio encoder architecture
+(Conv2dSubsampling → RelPositionalEncoding → N × RelPosEmbConformerBlock).
+This module factors out the common building blocks to avoid duplication.
+"""
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from vllm.model_executor.layers.linear import ReplicatedLinear
+
+
+class Conv2dSubsampling(nn.Module):
+    def __init__(self, idim: int, d_model: int, out_channels: int = 32):
+        super().__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(1, out_channels, 3, 2),
+            nn.ReLU(),
+            nn.Conv2d(out_channels, out_channels, 3, 2),
+            nn.ReLU(),
+        )
+        subsample_idim = ((idim - 1) // 2 - 1) // 2
+        self.out = ReplicatedLinear(
+            input_size=out_channels * subsample_idim,
+            output_size=d_model,
+            bias=True,
+        )
+
+        self.subsampling = 4
+        left_context = right_context = 3  # both exclude current frame
+        self.context = left_context + 1 + right_context  # 7
+
+    def forward(
+        self, x: torch.Tensor, x_mask: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        x = x.unsqueeze(1)
+        x = self.conv(x)
+        N, C, T, D = x.size()
+        x, _ = self.out(x.transpose(1, 2).contiguous().view(N, T, C * D))
+        mask = x_mask[:, :, :-2:2][:, :, :-2:2]
+        input_lengths = mask[:, -1, :].sum(dim=-1)
+        return x, input_lengths, mask
+
+
+class Swish(nn.Module):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x * torch.sigmoid(x)
+
+
+class RelPositionalEncoding(nn.Module):
+    def __init__(self, d_model: int, max_len: int = 5000):
+        super().__init__()
+        pe_positive = torch.zeros(max_len, d_model, requires_grad=False)
+        pe_negative = torch.zeros(max_len, d_model, requires_grad=False)
+        position = torch.arange(0, max_len).unsqueeze(1).float()
+        div_term = torch.exp(
+            torch.arange(0, d_model, 2).float()
+            * -(torch.log(torch.tensor(10000.0)).item() / d_model)
+        )
+        pe_positive[:, 0::2] = torch.sin(position * div_term)
+        pe_positive[:, 1::2] = torch.cos(position * div_term)
+        pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
+        pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
+
+        pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
+        pe_negative = pe_negative[1:].unsqueeze(0)
+        self.pe = torch.cat([pe_positive, pe_negative], dim=1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # Tmax = 2 * max_len - 1
+        Tmax, T = self.pe.size(1), x.size(1)
+        pos_emb = self.pe[:, Tmax // 2 - T + 1 : Tmax // 2 + T].clone().detach()
+        return pos_emb
+
+
+class ConformerFeedForward(nn.Module):
+    def __init__(self, d_model: int):
+        super().__init__()
+        self.pre_layer_norm = nn.LayerNorm(d_model)
+        self.linear_expand = ReplicatedLinear(
+            input_size=d_model,
+            output_size=d_model * 4,
+            bias=True,
+        )
+        self.nonlinear = Swish()
+        self.linear_project = ReplicatedLinear(
+            input_size=d_model * 4,
+            output_size=d_model,
+            bias=True,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        x = self.pre_layer_norm(x)
+        x, _ = self.linear_expand(x)
+        x = self.nonlinear(x)
+        x, _ = self.linear_project(x)
+        return x + residual
+
+
+class EncoderMultiHeadAttention(nn.Module):
+    def __init__(self, n_head: int, d_model: int):
+        super().__init__()
+        assert d_model % n_head == 0
+        self.n_head = n_head
+        self.d_k = d_model // n_head
+        self.d_v = self.d_k
+
+        self.w_qs = ReplicatedLinear(d_model, n_head * self.d_k, bias=False)
+        self.w_ks = ReplicatedLinear(d_model, n_head * self.d_k, bias=False)
+        self.w_vs = ReplicatedLinear(d_model, n_head * self.d_v, bias=False)
+
+        self.layer_norm_q = nn.LayerNorm(d_model)
+        self.layer_norm_k = nn.LayerNorm(d_model)
+        self.layer_norm_v = nn.LayerNorm(d_model)
+
+        self.fc = ReplicatedLinear(n_head * self.d_v, d_model, bias=False)
+
+    def forward_qkv(
+        self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
+        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
+
+        q = self.layer_norm_q(q)
+        k = self.layer_norm_k(k)
+        v = self.layer_norm_v(v)
+
+        q = self.w_qs(q)[0].view(sz_b, len_q, n_head, d_k)
+        k = self.w_ks(k)[0].view(sz_b, len_k, n_head, d_k)
+        v = self.w_vs(v)[0].view(sz_b, len_v, n_head, d_v)
+        q = q.transpose(1, 2)
+        k = k.transpose(1, 2)
+        v = v.transpose(1, 2)
+        return q, k, v
+
+    def forward_output(
+        self,
+        output: torch.Tensor,
+        residual: torch.Tensor,
+        sz_b: int,
+        len_q: int,
+    ) -> torch.Tensor:
+        output = output.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
+        fc_out, _ = self.fc(output)
+        return fc_out + residual
+
+    def forward_attention(
+        self,
+        attn: torch.Tensor,
+        v: torch.Tensor,
+        mask: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if mask is not None:
+            mask = mask.unsqueeze(1)
+            mask = mask.eq(0)
+            attn = attn.masked_fill(mask, -float("inf"))
+            attn = torch.softmax(attn, dim=-1).masked_fill(mask, 0.0)
+        else:
+            attn = torch.softmax(attn, dim=-1)
+        output = torch.matmul(attn, v)
+        return output, attn
+
+
+class RelPosMultiHeadAttention(EncoderMultiHeadAttention):
+    def __init__(self, n_head: int, d_model: int):
+        super().__init__(n_head, d_model)
+        d_k = d_model // n_head
+        self.scale = 1.0 / (d_k**0.5)
+        self.linear_pos = ReplicatedLinear(d_model, n_head * d_k, bias=False)
+        self.pos_bias_u = nn.Parameter(torch.empty([n_head, d_k]))
+        self.pos_bias_v = nn.Parameter(torch.empty([n_head, d_k]))
+
+    def _rel_shift(self, x):
+        N, H, T1, T2 = x.size()
+        zero_pad = torch.zeros((N, H, T1, 1), device=x.device, dtype=x.dtype)
+        x_padded = torch.cat([zero_pad, x], dim=-1)
+        x_padded = x_padded.view(N, H, T2 + 1, T1)
+        x = x_padded[:, :, 1:].view_as(x)
+        x = x[:, :, :, : x.size(-1) // 2 + 1]
+        return x
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        pos_emb: torch.Tensor,
+        mask: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        sz_b, len_q = q.size(0), q.size(1)
+        residual = q
+        q, k, v = self.forward_qkv(q, k, v)
+
+        q = q.transpose(1, 2)
+        n_batch_pos = pos_emb.size(0)
+        p = self.linear_pos(pos_emb)[0].view(n_batch_pos, -1, self.n_head, self.d_k)
+        p = p.transpose(1, 2)
+
+        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
+        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
+
+        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
+        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+        matrix_bd = self._rel_shift(matrix_bd)
+
+        attn_scores = matrix_ac + matrix_bd
+        attn_scores.mul_(self.scale)
+
+        output, attn = self.forward_attention(attn_scores, v, mask=mask)
+        output = self.forward_output(output, residual, sz_b, len_q)
+        return output, attn
+
+
+class ConformerConvolution(nn.Module):
+    def __init__(self, d_model: int, kernel_size: int = 33):
+        super().__init__()
+        assert kernel_size % 2 == 1
+        self.pre_layer_norm = nn.LayerNorm(d_model)
+        self.pointwise_conv1 = nn.Conv1d(
+            d_model, d_model * 4, kernel_size=1, bias=False
+        )
+        self.padding = (kernel_size - 1) // 2
+        self.depthwise_conv = nn.Conv1d(
+            d_model * 2,
+            d_model * 2,
+            kernel_size,
+            stride=1,
+            padding=self.padding,
+            groups=d_model * 2,
+            bias=False,
+        )
+        self.batch_norm = nn.LayerNorm(d_model * 2)
+        self.swish = Swish()
+        self.pointwise_conv2 = nn.Conv1d(
+            d_model * 2, d_model, kernel_size=1, bias=False
+        )
+
+    def forward(
+        self, x: torch.Tensor, mask: torch.Tensor | None = None
+    ) -> torch.Tensor:
+        residual = x
+        out = self.pre_layer_norm(x)
+        out = out.transpose(1, 2)
+        if mask is not None:
+            out.masked_fill_(mask.ne(1), 0.0)
+        out = self.pointwise_conv1(out)
+        out = F.glu(out, dim=1)
+        out = self.depthwise_conv(out)
+        out = out.transpose(1, 2)
+        out = self.swish(self.batch_norm(out))
+        out = out.transpose(1, 2)
+        out = self.pointwise_conv2(out)
+        if mask is not None:
+            out.masked_fill_(mask.ne(1), 0.0)
+        out = out.transpose(1, 2)
+        return out + residual
+
+
+class RelPosEmbConformerBlock(nn.Module):
+    def __init__(self, d_model: int, n_head: int, kernel_size: int = 33):
+        super().__init__()
+        self.ffn1 = ConformerFeedForward(d_model)
+        self.mhsa = RelPosMultiHeadAttention(n_head, d_model)
+        self.conv = ConformerConvolution(d_model, kernel_size)
+        self.ffn2 = ConformerFeedForward(d_model)
+        self.layer_norm = nn.LayerNorm(d_model)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        pos_emb: torch.Tensor,
+        slf_attn_mask: torch.Tensor | None = None,
+        pad_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        out = 0.5 * x + 0.5 * self.ffn1(x)
+        out = self.mhsa(out, out, out, pos_emb, mask=slf_attn_mask)[0]
+        out = self.conv(out, pad_mask)
+        out = 0.5 * out + 0.5 * self.ffn2(out)
+        out = self.layer_norm(out)
+        return out
+
+
+class ConformerEncoder(nn.Module):
+    """
+    Conformer encoder shared by FireRedASR2 and FireRedLID.
+    """
+
+    def __init__(
+        self,
+        idim: int,
+        n_layers_enc: int,
+        n_head: int,
+        d_model: int,
+        kernel_size: int = 33,
+        pe_maxlen: int = 5000,
+    ):
+        super().__init__()
+        self.odim = d_model
+
+        self.input_preprocessor = Conv2dSubsampling(idim, d_model)
+        self.positional_encoding = RelPositionalEncoding(d_model, max_len=pe_maxlen)
+
+        self.layer_stack = nn.ModuleList()
+        for _ in range(n_layers_enc):
+            block = RelPosEmbConformerBlock(d_model, n_head, kernel_size)
+            self.layer_stack.append(block)
+
+    def forward(
+        self,
+        padded_input: torch.Tensor,
+        input_lengths: torch.Tensor,
+        pad: bool = True,
+    ):
+        if pad:
+            padded_input = F.pad(
+                padded_input,
+                (0, 0, 0, self.input_preprocessor.context - 1),
+                "constant",
+                0.0,
+            )
+        src_mask = self.padding_position_is_0(padded_input, input_lengths)
+
+        embed_output, input_lengths, src_mask = self.input_preprocessor(
+            padded_input, src_mask
+        )
+        enc_output = embed_output
+
+        pos_emb = self.positional_encoding(embed_output)
+
+        for enc_layer in self.layer_stack:
+            enc_output = enc_layer(
+                enc_output, pos_emb, slf_attn_mask=src_mask, pad_mask=src_mask
+            )
+
+        return enc_output, input_lengths, src_mask
+
+    def padding_position_is_0(
+        self, padded_input: torch.Tensor, input_lengths: torch.Tensor
+    ) -> torch.Tensor:
+        N, T = padded_input.size()[:2]
+        # Use broadcasting instead of a Python loop for efficiency.
+        positions = torch.arange(T, device=padded_input.device).unsqueeze(0)
+        mask = (positions < input_lengths.unsqueeze(1)).to(torch.uint8)
+        return mask.unsqueeze(1)

vllm/model_executor/models/fireredasr2.py

@@ -6,7 +6,6 @@ from typing import Annotated, Literal, cast
 
 import numpy as np
 import torch
-import torch.nn.functional as F
 from torch import nn
 from transformers import (
     BatchFeature,
@@ -45,6 +44,7 @@ from vllm.transformers_utils.processors.fireredasr2 import (
 )
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
+from .conformer_encoder import ConformerEncoder
 from .interfaces import (
     MultiModalEmbeddings,
     SupportsMultiModal,
@@ -84,352 +84,6 @@ class FireRedASR2AudioInputs(TensorSchema):
     ]
 
 
-class Swish(nn.Module):
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return x * torch.sigmoid(x)
-
-
-class Conv2dSubsampling(nn.Module):
-    def __init__(self, idim: int, d_model: int, out_channels: int = 32):
-        super().__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(1, out_channels, 3, 2),
-            nn.ReLU(),
-            nn.Conv2d(out_channels, out_channels, 3, 2),
-            nn.ReLU(),
-        )
-        subsample_idim = ((idim - 1) // 2 - 1) // 2
-        self.out = ReplicatedLinear(
-            input_size=out_channels * subsample_idim,
-            output_size=d_model,
-            bias=True,
-        )
-
-        self.subsampling = 4
-        left_context = right_context = 3  # both exclude current frame
-        self.context = left_context + 1 + right_context  # 7
-
-    def forward(
-        self, x: torch.Tensor, x_mask: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        x = x.unsqueeze(1)
-        x = self.conv(x)
-        N, C, T, D = x.size()
-        x, _ = self.out(x.transpose(1, 2).contiguous().view(N, T, C * D))
-        mask = x_mask[:, :, :-2:2][:, :, :-2:2]
-        input_lengths = mask[:, -1, :].sum(dim=-1)
-        return x, input_lengths, mask
-
-
-class RelPositionalEncoding(nn.Module):
-    def __init__(self, d_model: int, max_len: int = 5000):
-        super().__init__()
-        pe_positive = torch.zeros(max_len, d_model, requires_grad=False)
-        pe_negative = torch.zeros(max_len, d_model, requires_grad=False)
-        position = torch.arange(0, max_len).unsqueeze(1).float()
-        div_term = torch.exp(
-            torch.arange(0, d_model, 2).float()
-            * -(torch.log(torch.tensor(10000.0)).item() / d_model)
-        )
-        pe_positive[:, 0::2] = torch.sin(position * div_term)
-        pe_positive[:, 1::2] = torch.cos(position * div_term)
-        pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
-        pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
-
-        pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
-        pe_negative = pe_negative[1:].unsqueeze(0)
-        self.pe = torch.cat([pe_positive, pe_negative], dim=1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        # Tmax = 2 * max_len - 1
-        Tmax, T = self.pe.size(1), x.size(1)
-        pos_emb = self.pe[:, Tmax // 2 - T + 1 : Tmax // 2 + T].clone().detach()
-        return pos_emb
-
-
-class ConformerFeedForward(nn.Module):
-    def __init__(self, d_model: int):
-        super().__init__()
-        self.pre_layer_norm = nn.LayerNorm(d_model)
-        self.linear_expand = ReplicatedLinear(
-            input_size=d_model,
-            output_size=d_model * 4,
-            bias=True,
-        )
-        self.nonlinear = Swish()
-        self.linear_project = ReplicatedLinear(
-            input_size=d_model * 4,
-            output_size=d_model,
-            bias=True,
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        residual = x
-        x = self.pre_layer_norm(x)
-        x, _ = self.linear_expand(x)
-        x = self.nonlinear(x)
-        x, _ = self.linear_project(x)
-        output = x + residual
-        return output
-
-
-class EncoderMultiHeadAttention(nn.Module):
-    def __init__(self, n_head: int, d_model: int):
-        super().__init__()
-        assert d_model % n_head == 0
-        self.n_head = n_head
-        self.d_k = d_model // n_head
-        self.d_v = self.d_k
-
-        self.w_qs = ReplicatedLinear(
-            input_size=d_model, output_size=n_head * self.d_k, bias=False
-        )
-        self.w_ks = ReplicatedLinear(
-            input_size=d_model, output_size=n_head * self.d_k, bias=False
-        )
-        self.w_vs = ReplicatedLinear(
-            input_size=d_model, output_size=n_head * self.d_v, bias=False
-        )
-
-        self.layer_norm_q = nn.LayerNorm(d_model)
-        self.layer_norm_k = nn.LayerNorm(d_model)
-        self.layer_norm_v = nn.LayerNorm(d_model)
-
-        self.fc = ReplicatedLinear(
-            input_size=n_head * self.d_v, output_size=d_model, bias=False
-        )
-
-    def forward_qkv(
-        self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
-        sz_b, len_q, len_k, len_v = q.size(0), q.size(1), k.size(1), v.size(1)
-
-        q = self.layer_norm_q(q)
-        k = self.layer_norm_k(k)
-        v = self.layer_norm_v(v)
-
-        q = self.w_qs(q)[0].view(sz_b, len_q, n_head, d_k)
-        k = self.w_ks(k)[0].view(sz_b, len_k, n_head, d_k)
-        v = self.w_vs(v)[0].view(sz_b, len_v, n_head, d_v)
-        q = q.transpose(1, 2)
-        k = k.transpose(1, 2)
-        v = v.transpose(1, 2)
-        return q, k, v
-
-    def forward_output(
-        self, output: torch.Tensor, residual: torch.Tensor, sz_b: int, len_q: int
-    ) -> torch.Tensor:
-        output = output.transpose(1, 2).contiguous().view(sz_b, len_q, -1)
-        fc_out, _ = self.fc(output)
-        output = fc_out
-        output = output + residual
-        return output
-
-    def forward_attention(
-        self, attn: torch.Tensor, v: torch.Tensor, mask: torch.Tensor | None = None
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        if mask is not None:
-            mask = mask.unsqueeze(1)
-            mask = mask.eq(0)
-            attn = attn.masked_fill(mask, -float("inf"))
-            attn = torch.softmax(attn, dim=-1).masked_fill(mask, 0.0)
-        else:
-            attn = torch.softmax(attn, dim=-1)
-
-        d_attn = attn
-        output = torch.matmul(d_attn, v)
-
-        return output, attn
-
-
-class RelPosMultiHeadAttention(EncoderMultiHeadAttention):
-    def __init__(self, n_head: int, d_model: int):
-        super().__init__(n_head, d_model)
-        d_k = d_model // n_head
-        self.scale = 1.0 / (d_k**0.5)
-        self.linear_pos = ReplicatedLinear(
-            input_size=d_model, output_size=n_head * d_k, bias=False
-        )
-        self.pos_bias_u = nn.Parameter(torch.empty([n_head, d_k]))
-        self.pos_bias_v = nn.Parameter(torch.empty([n_head, d_k]))
-
-    def _rel_shift(self, x):
-        N, H, T1, T2 = x.size()
-        zero_pad = torch.zeros((N, H, T1, 1), device=x.device, dtype=x.dtype)
-        x_padded = torch.cat([zero_pad, x], dim=-1)
-
-        x_padded = x_padded.view(N, H, T2 + 1, T1)
-        x = x_padded[:, :, 1:].view_as(x)
-        x = x[:, :, :, : x.size(-1) // 2 + 1]
-        return x
-
-    def forward(
-        self,
-        q: torch.Tensor,
-        k: torch.Tensor,
-        v: torch.Tensor,
-        pos_emb: torch.Tensor,
-        mask: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        sz_b, len_q = q.size(0), q.size(1)
-
-        residual = q
-        q, k, v = self.forward_qkv(q, k, v)
-
-        q = q.transpose(1, 2)
-        n_batch_pos = pos_emb.size(0)
-        p = self.linear_pos(pos_emb)[0].view(n_batch_pos, -1, self.n_head, self.d_k)
-        p = p.transpose(1, 2)
-
-        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
-        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
-
-        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
-
-        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
-        matrix_bd = self._rel_shift(matrix_bd)
-
-        attn_scores = matrix_ac + matrix_bd
-        attn_scores.mul_(self.scale)
-
-        output, attn = self.forward_attention(attn_scores, v, mask=mask)
-
-        output = self.forward_output(output, residual, sz_b, len_q)
-        return output, attn
-
-
-class ConformerConvolution(nn.Module):
-    def __init__(self, d_model: int, kernel_size: int = 33):
-        super().__init__()
-        assert kernel_size % 2 == 1
-        self.pre_layer_norm = nn.LayerNorm(d_model)
-        self.pointwise_conv1 = nn.Conv1d(
-            d_model, d_model * 4, kernel_size=1, bias=False
-        )
-        self.padding = (kernel_size - 1) // 2
-        self.depthwise_conv = nn.Conv1d(
-            d_model * 2,
-            d_model * 2,
-            kernel_size,
-            stride=1,
-            padding=self.padding,
-            groups=d_model * 2,
-            bias=False,
-        )
-        self.batch_norm = nn.LayerNorm(d_model * 2)
-        self.swish = Swish()
-        self.pointwise_conv2 = nn.Conv1d(
-            d_model * 2, d_model, kernel_size=1, bias=False
-        )
-
-    def forward(
-        self, x: torch.Tensor, mask: torch.Tensor | None = None
-    ) -> torch.Tensor:
-        residual = x
-        out = self.pre_layer_norm(x)
-        out = out.transpose(1, 2)
-        if mask is not None:
-            out.masked_fill_(mask.ne(1), 0.0)
-        out = self.pointwise_conv1(out)
-        out = F.glu(out, dim=1)
-        out = self.depthwise_conv(out)
-
-        out = out.transpose(1, 2)
-        out = self.swish(self.batch_norm(out))
-        out = out.transpose(1, 2)
-
-        out = self.pointwise_conv2(out)
-        if mask is not None:
-            out.masked_fill_(mask.ne(1), 0.0)
-        out = out.transpose(1, 2)
-        return out + residual
-
-
-class RelPosEmbConformerBlock(nn.Module):
-    def __init__(self, d_model, n_head, kernel_size=33):
-        super().__init__()
-        self.ffn1 = ConformerFeedForward(d_model)
-        self.mhsa = RelPosMultiHeadAttention(n_head, d_model)
-        self.conv = ConformerConvolution(d_model, kernel_size)
-        self.ffn2 = ConformerFeedForward(d_model)
-        self.layer_norm = nn.LayerNorm(d_model)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        pos_emb: torch.Tensor,
-        slf_attn_mask: torch.Tensor | None = None,
-        pad_mask: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        out = 0.5 * x + 0.5 * self.ffn1(x)
-        out = self.mhsa(out, out, out, pos_emb, mask=slf_attn_mask)[0]
-        out = self.conv(out, pad_mask)
-        out = 0.5 * out + 0.5 * self.ffn2(out)
-        out = self.layer_norm(out)
-        return out
-
-
-class ConformerEncoder(nn.Module):
-    def __init__(
-        self,
-        idim: int,
-        n_layers_enc: int,
-        n_head: int,
-        d_model: int,
-        kernel_size: int = 33,
-        pe_maxlen: int = 5000,
-    ):
-        super().__init__()
-        self.odim = d_model
-
-        self.input_preprocessor = Conv2dSubsampling(idim, d_model)
-        self.positional_encoding = RelPositionalEncoding(d_model)
-
-        self.layer_stack = nn.ModuleList()
-        for _ in range(n_layers_enc):
-            block = RelPosEmbConformerBlock(d_model, n_head, kernel_size)
-            self.layer_stack.append(block)
-
-    def forward(
-        self, padded_input: torch.Tensor, input_lengths: torch.Tensor, pad: bool = True
-    ):
-        if pad:
-            padded_input = F.pad(
-                padded_input,
-                (0, 0, 0, self.input_preprocessor.context - 1),
-                "constant",
-                0.0,
-            )
-        src_mask = self.padding_position_is_0(padded_input, input_lengths)
-
-        embed_output, input_lengths, src_mask = self.input_preprocessor(
-            padded_input, src_mask
-        )
-        enc_output = embed_output
-
-        pos_emb = self.positional_encoding(embed_output)
-
-        enc_outputs = []
-        for enc_layer in self.layer_stack:
-            enc_output = enc_layer(
-                enc_output, pos_emb, slf_attn_mask=src_mask, pad_mask=src_mask
-            )
-            enc_outputs.append(enc_output)
-
-        return enc_output, input_lengths, src_mask
-
-    def padding_position_is_0(
-        self, padded_input: torch.Tensor, input_lengths: torch.Tensor
-    ) -> torch.Tensor:
-        N, T = padded_input.size()[:2]
-        mask = torch.ones((N, T)).to(padded_input.device)
-        for i in range(N):
-            mask[i, input_lengths[i] :] = 0
-        mask = mask.unsqueeze(dim=1)
-        return mask.to(torch.uint8)
-
-
 class FireRedASR2Adapter(nn.Module):
     def __init__(self, encoder_dim: int, llm_dim: int, downsample_rate: int = 2):
         super().__init__()

vllm/model_executor/models/registry.py

@@ -380,6 +380,10 @@ _MULTIMODAL_MODELS = {
         "FireRedASR2ForConditionalGeneration",
     ),
     "FunASRForConditionalGeneration": ("funasr", "FunASRForConditionalGeneration"),
+    "FireRedLIDForConditionalGeneration": (
+        "fireredlid",
+        "FireRedLIDForConditionalGeneration",
+    ),
     "FunAudioChatForConditionalGeneration": (
         "funaudiochat",
         "FunAudioChatForConditionalGeneration",

vllm/transformers_utils/config.py

@@ -90,6 +90,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     deepseek_vl_v2="DeepseekVLV2Config",
     deepseek_v32="DeepseekV3Config",
     flex_olmo="FlexOlmoConfig",
+    fireredlid="FireRedLIDConfig",
     funaudiochat="FunAudioChatConfig",
     hunyuan_vl="HunYuanVLConfig",
     isaac="IsaacConfig",

vllm/transformers_utils/configs/__init__.py

@@ -28,6 +28,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "DeepseekVLV2Config": "vllm.transformers_utils.configs.deepseek_vl2",
     "DotsOCRConfig": "vllm.transformers_utils.configs.dotsocr",
     "EAGLEConfig": "vllm.transformers_utils.configs.eagle",
+    "FireRedLIDConfig": "vllm.transformers_utils.configs.fireredlid",
     "FlexOlmoConfig": "vllm.transformers_utils.configs.flex_olmo",
     "FunAudioChatConfig": "vllm.transformers_utils.configs.funaudiochat",
     "FunAudioChatAudioEncoderConfig": "vllm.transformers_utils.configs.funaudiochat",
@@ -88,6 +89,7 @@ __all__ = [
     "DotsOCRConfig",
     "EAGLEConfig",
     "FlexOlmoConfig",
+    "FireRedLIDConfig",
     "FunAudioChatConfig",
     "FunAudioChatAudioEncoderConfig",
     "HunYuanVLConfig",

vllm/transformers_utils/configs/fireredlid.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+import contextlib
+
+from transformers import AutoConfig
+from transformers.configuration_utils import PretrainedConfig
+
+
+class FireRedLIDConfig(PretrainedConfig):
+    """Minimal config class for native vLLM FireRedLID support."""
+
+    model_type = "fireredlid"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size: int = 120,
+        lid_odim: int = 120,
+        idim: int = 80,
+        d_model: int = 1280,
+        n_head: int = 20,
+        n_layers_enc: int = 16,
+        n_layers_lid_dec: int = 6,
+        kernel_size: int = 33,
+        residual_dropout: float = 0.05,
+        dropout_rate: float = 0.05,
+        pe_maxlen: int = 5000,
+        pad_token_id: int = 2,
+        bos_token_id: int = 3,
+        eos_token_id: int = 4,
+        decoder_start_token_id: int = 3,
+        tie_word_embeddings: bool = True,
+        is_encoder_decoder: bool = True,
+        architectures: list[str] | None = None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.lid_odim = lid_odim
+        self.idim = idim
+        self.d_model = d_model
+        self.hidden_size = d_model
+        self.n_head = n_head
+        self.num_attention_heads = n_head
+        self.n_layers_enc = n_layers_enc
+        self.encoder_layers = n_layers_enc
+        self.n_layers_lid_dec = n_layers_lid_dec
+        self.decoder_layers = n_layers_lid_dec
+        self.num_hidden_layers = n_layers_lid_dec
+        self.kernel_size = kernel_size
+        self.residual_dropout = residual_dropout
+        self.dropout_rate = dropout_rate
+        self.pe_maxlen = pe_maxlen
+        self.tie_word_embeddings = tie_word_embeddings
+        self.is_encoder_decoder = is_encoder_decoder
+        self.architectures = architectures or ["FireRedLIDForConditionalGeneration"]
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            decoder_start_token_id=decoder_start_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            is_encoder_decoder=is_encoder_decoder,
+            architectures=self.architectures,
+            **kwargs,
+        )
+
+
+with contextlib.suppress(ValueError):
+    AutoConfig.register(FireRedLIDConfig.model_type, FireRedLIDConfig)

vllm/transformers_utils/processors/__init__.py

@@ -16,6 +16,7 @@ __all__ = [
     "CohereASRProcessor",
     "DeepseekVLV2Processor",
     "FireRedASR2Processor",
+    "FireRedLIDProcessor",
     "FunASRProcessor",
     "GLM4VProcessor",
     "H2OVLProcessor",
@@ -44,6 +45,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "CohereASRProcessor": "vllm.transformers_utils.processors.cohere_asr",
     "DeepseekVLV2Processor": "vllm.transformers_utils.processors.deepseek_vl2",
     "FireRedASR2Processor": "vllm.transformers_utils.processors.fireredasr2",
+    "FireRedLIDProcessor": "vllm.transformers_utils.processors.fireredlid",
     "FunASRProcessor": "vllm.transformers_utils.processors.funasr",
     "GLM4VProcessor": "vllm.transformers_utils.processors.glm4v",
     "H2OVLProcessor": "vllm.transformers_utils.processors.h2ovl",

vllm/transformers_utils/processors/fireredlid.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+FireRedLID feature extractor and processor.
+
+The FeatureExtractor handles:
+  - Raw waveform → 80-dim log-mel filterbank (via kaldi_native_fbank)
+  - CMVN normalization (means / inverse_std_variences from preprocessor_config)
+  - Padding + length tracking
+
+The Processor wraps the FeatureExtractor and a tokenizer.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from transformers import (
+    AutoFeatureExtractor,
+    BatchFeature,
+)
+from transformers.feature_extraction_sequence_utils import SequenceFeatureExtractor
+from transformers.processing_utils import ProcessorMixin
+from transformers.utils import TensorType
+
+from vllm.logger import init_logger
+from vllm.utils.import_utils import LazyLoader
+
+if TYPE_CHECKING:
+    import kaldi_native_fbank as knf
+else:
+    knf = LazyLoader("knf", globals(), "kaldi_native_fbank")
+
+
+logger = init_logger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Helpers (shared with FireRedASR2 processor)
+# ---------------------------------------------------------------------------
+
+
+class CMVN:
+    def __init__(self, dim, means, inverse_std_variences):
+        self.dim = dim
+        self.means = np.array(means)
+        self.inverse_std_variences = np.array(inverse_std_variences)
+
+    def __call__(self, x):
+        assert x.shape[-1] == self.dim, "CMVN dim mismatch"
+        out = x - self.means
+        out = out * self.inverse_std_variences
+        return out
+
+
+class KaldifeatFbank:
+    def __init__(
+        self,
+        num_mel_bins: int = 80,
+        frame_length: int = 25,
+        frame_shift: int = 10,
+        dither: float = 0.0,
+    ):
+        self.dither = dither
+        opts = knf.FbankOptions()
+        opts.frame_opts.dither = dither
+        opts.mel_opts.num_bins = num_mel_bins
+        opts.frame_opts.snip_edges = True
+        opts.mel_opts.debug_mel = False
+        self.opts = opts
+
+    def __call__(self, sample_rate, wav_np, is_train=False):
+        dither = self.dither if is_train else 0.0
+        self.opts.frame_opts.dither = dither
+        fbank = knf.OnlineFbank(self.opts)
+        fbank.accept_waveform(sample_rate, wav_np.tolist())
+        feat = []
+        for i in range(fbank.num_frames_ready):
+            feat.append(fbank.get_frame(i))
+        if len(feat) == 0:
+            return np.zeros((0, self.opts.mel_opts.num_bins))
+        return np.vstack(feat)
+
+
+# ---------------------------------------------------------------------------
+# Feature Extractor
+# ---------------------------------------------------------------------------
+
+
+class FireRedLIDFeatureExtractor(SequenceFeatureExtractor):
+    """
+    Extracts 80-dim log-mel filterbank features from raw waveforms,
+    applies CMVN, and returns padded feature tensors with lengths.
+
+    Also computes ``fake_token_lengths`` — the actual encoder output
+    length for each audio — so that vLLM can allocate the correct
+    number of cross-attention KV cache slots.
+    """
+
+    model_input_names = ["input_features"]
+
+    def __init__(
+        self,
+        feature_size=80,
+        sampling_rate=16000,
+        chunk_length=30,
+        padding_value=0.0,
+        return_attention_mask=False,
+        dim=80,
+        means=None,
+        inverse_std_variences=None,
+        num_mel_bins=80,
+        frame_length=25,
+        frame_shift=10,
+        dither=0.0,
+        left_context=3,
+        right_context=3,
+        **kwargs,
+    ):
+        super().__init__(
+            feature_size=feature_size,
+            sampling_rate=sampling_rate,
+            padding_value=padding_value,
+            return_attention_mask=return_attention_mask,
+            **kwargs,
+        )
+        self.chunk_length = chunk_length
+        self.dim = dim
+        self.means = means
+        self.inverse_std_variences = inverse_std_variences
+        self.num_mel_bins = num_mel_bins
+        self.frame_length = frame_length
+        self.frame_shift = frame_shift
+        self.dither = dither
+        self.sampling_rate = sampling_rate
+        self.context = left_context + 1 + right_context
+
+    def __call__(
+        self,
+        raw_speech: np.ndarray | list[float] | list[np.ndarray] | list[list[float]],
+        truncation: bool = True,
+        pad_to_multiple_of: int | None = None,
+        return_tensors: str | TensorType | None = None,
+        return_attention_mask: bool | None = None,
+        padding: str | None = "max_length",
+        max_length: int | None = None,
+        sampling_rate: int | None = None,
+        do_normalize: bool | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        if sampling_rate is not None and sampling_rate != self.sampling_rate:
+            raise ValueError(
+                f"FireRedLIDFeatureExtractor expects sampling_rate="
+                f"{self.sampling_rate}, got {sampling_rate}."
+            )
+
+        # Initialize helpers
+        cmvn = CMVN(self.dim, self.means, self.inverse_std_variences)
+        fbank = KaldifeatFbank(
+            num_mel_bins=self.num_mel_bins,
+            frame_length=self.frame_length,
+            frame_shift=self.frame_shift,
+            dither=self.dither,
+        )
+
+        def padding_position_is_0(padded_input, input_lengths):
+            N, T = padded_input.size()[:2]
+            mask = torch.ones((N, T)).to(padded_input.device)
+            for i in range(N):
+                mask[i, input_lengths[i] :] = 0
+            mask = mask.unsqueeze(dim=1)
+            return mask.to(torch.uint8)
+
+        feats = []
+        speech_lengths = []
+        fake_token_lengths = []
+
+        for speech in raw_speech:
+            # vLLM loads audio via librosa (float32 in [-1,1]),
+            # but kaldi_native_fbank expects int16-scale values.
+            speech_scaled = speech * 32768
+            feat = fbank(self.sampling_rate, speech_scaled)
+            feat = cmvn(feat)
+            feat = torch.from_numpy(feat).float()
+            length = feat.size(0)
+            feats.append(feat)
+            speech_lengths.append(length)
+
+            # Compute the actual Conv2dSubsampling output length.
+            # This mirrors the mask logic in Conv2dSubsampling.forward:
+            #   pad context frames, then mask[:, :, :-2:2][:, :, :-2:2].sum()
+            padded_input = F.pad(feat, (0, 0, 0, self.context - 1), "constant", 0.0)
+            src_mask = padding_position_is_0(
+                padded_input[None, :, :],
+                torch.tensor([length], dtype=torch.int32),
+            )
+            mask = src_mask[:, :, :-2:2][:, :, :-2:2]
+            enc_len = mask[:, -1, :].sum(dim=-1)
+            fake_token_len = torch.clamp(enc_len, min=1)
+            fake_token_lengths.append(fake_token_len)
+
+        if len(feats) == 0:
+            return BatchFeature()
+
+        # Pad to uniform length
+        max_feat_len = max(f.size(0) for f in feats)
+        padded = feats[0].new_zeros(len(feats), max_feat_len, feats[0].size(1))
+        for i, feat in enumerate(feats):
+            padded[i, : feat.size(0)] = feat
+
+        result = BatchFeature({"input_features": padded})
+
+        if return_tensors is not None:
+            result = result.convert_to_tensors(return_tensors)
+
+        result["speech_lengths"] = torch.tensor(speech_lengths, dtype=torch.long)
+        result["fake_token_lengths"] = torch.concat(fake_token_lengths)
+        return result
+
+
+# ---------------------------------------------------------------------------
+# Processor
+# ---------------------------------------------------------------------------
+
+
+class FireRedLIDProcessor(ProcessorMixin):
+    """
+    Wraps FireRedLIDFeatureExtractor + a tokenizer.
+    """
+
+    feature_extractor_class = "FireRedLIDFeatureExtractor"
+    tokenizer_class = ("PreTrainedTokenizer", "PreTrainedTokenizerFast")
+
+    def __init__(self, feature_extractor, tokenizer):
+        super().__init__(feature_extractor, tokenizer)
+        self.current_processor = self.feature_extractor
+        self._in_target_context_manager = False
+
+    def __call__(self, *args, **kwargs):
+        if self._in_target_context_manager:
+            return self.current_processor(*args, **kwargs)
+
+        audio = kwargs.pop("audio", None)
+        sampling_rate = kwargs.pop("sampling_rate", None)
+        text = kwargs.pop("text", None)
+        if len(args) > 0:
+            audio = args[0]
+            args = args[1:]
+
+        if audio is not None:
+            inputs = self.feature_extractor(
+                audio, *args, sampling_rate=sampling_rate, **kwargs
+            )
+        else:
+            inputs = BatchFeature()
+
+        if text is not None:
+            if isinstance(text, str):
+                text = [text]
+            encodings = self.tokenizer(text, **kwargs)
+            if audio is not None:
+                inputs["labels"] = encodings["input_ids"]
+            else:
+                return encodings
+
+        return inputs
+
+
+# ---------------------------------------------------------------------------
+# Registration
+# ---------------------------------------------------------------------------
+
+AutoFeatureExtractor.register("FireRedLIDFeatureExtractor", FireRedLIDFeatureExtractor)