Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.

runtime/triton_gpu/model_repo_sense_voice_small/encoder/1/model.onnx

@@ -0,0 +1 @@
+/mnt/samsung-t7/yuekai/asr/funaudiollm/SenseVoice/model.onnx

runtime/triton_gpu/model_repo_sense_voice_small/encoder/config.pbtxt

@@ -0,0 +1,71 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+
+name: "encoder"
+backend: "onnxruntime"
+default_model_filename: "model.onnx"
+
+max_batch_size: 16
+
+input [
+  {
+    name: "speech"
+    data_type: TYPE_FP32
+    dims: [-1, 560]
+  },
+  {
+    name: "speech_lengths"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  },
+  {
+    name: "language"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  },
+  {
+    name: "textnorm"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  }
+]
+
+output [
+  {
+    name: "ctc_logits"
+    data_type: TYPE_FP32
+    dims: [-1, 25055] 
+  },
+  {
+    name: "encoder_out_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  }
+]
+
+dynamic_batching {
+  }
+parameters { key: "cudnn_conv_algo_search" value: { string_value: "2" } }
+
+instance_group [
+    {
+      count: 1
+      kind: KIND_GPU
+    }
+]
+

runtime/triton_gpu/model_repo_sense_voice_small/feature_extractor/1/model.py

@@ -0,0 +1,325 @@
+#!/bin/bash
+#
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+#
+# 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 math
+import triton_python_backend_utils as pb_utils
+from torch.utils.dlpack import to_dlpack
+import torch
+import numpy as np
+import kaldifeat
+import _kaldifeat
+from typing import List
+import json
+import yaml
+from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
+
+
+class LFR(torch.nn.Module):
+    """Batch LFR: https://github.com/Mddct/devil-asr/blob/main/patch/lfr.py"""
+
+    def __init__(self, m: int = 7, n: int = 6) -> None:
+        """
+        Actually, this implements stacking frames and skipping frames.
+        if m = 1 and n = 1, just return the origin features.
+        if m = 1 and n > 1, it works like skipping.
+        if m > 1 and n = 1, it works like stacking but only support right frames.
+        if m > 1 and n > 1, it works like LFR.
+        """
+        super().__init__()
+
+        self.m = m
+        self.n = n
+
+        self.left_padding_nums = math.ceil((self.m - 1) // 2)
+
+    def forward(
+        self, input_tensor: torch.Tensor, input_lens: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        B, _, D = input_tensor.size()
+        n_lfr = torch.ceil(input_lens / self.n)
+
+        prepad_nums = input_lens + self.left_padding_nums
+
+        right_padding_nums = torch.where(
+            self.m >= (prepad_nums - self.n * (n_lfr - 1)),
+            self.m - (prepad_nums - self.n * (n_lfr - 1)),
+            0,
+        )
+
+        T_all = self.left_padding_nums + input_lens + right_padding_nums
+
+        new_len = T_all // self.n
+
+        T_all_max = T_all.max().int()
+
+        tail_frames_index = (input_lens - 1).view(B, 1, 1).repeat(1, 1, D)  # [B,1,D]
+
+        tail_frames = torch.gather(input_tensor, 1, tail_frames_index)
+        tail_frames = tail_frames.repeat(1, right_padding_nums.max().int(), 1)
+        head_frames = input_tensor[:, 0:1, :].repeat(1, self.left_padding_nums, 1)
+
+        # stack
+        input_tensor = torch.cat([head_frames, input_tensor, tail_frames], dim=1)
+
+        index = (
+            torch.arange(T_all_max, device=input_tensor.device, dtype=input_lens.dtype)
+            .unsqueeze(0)
+            .repeat(B, 1)
+        )  # [B, T_all_max]
+        index_mask = index < (self.left_padding_nums + input_lens).unsqueeze(1)  # [B, T_all_max]
+
+        tail_index_mask = torch.logical_not(index >= (T_all.unsqueeze(1))) & index_mask
+        tail = torch.ones(T_all_max, dtype=input_lens.dtype, device=input_tensor.device).unsqueeze(
+            0
+        ).repeat(B, 1) * (
+            T_all_max - 1
+        )  # [B, T_all_max]
+        indices = torch.where(torch.logical_or(index_mask, tail_index_mask), index, tail)
+        input_tensor = torch.gather(input_tensor, 1, indices.unsqueeze(2).repeat(1, 1, D))
+
+        input_tensor = input_tensor.unfold(1, self.m, step=self.n).transpose(2, 3)
+
+        return input_tensor.reshape(B, -1, D * self.m), new_len
+
+
+class WavFrontend:
+    """Conventional frontend structure for ASR."""
+
+    def __init__(
+        self,
+        cmvn_file: str = None,
+        fs: int = 16000,
+        window: str = "hamming",
+        n_mels: int = 80,
+        frame_length: int = 25,
+        frame_shift: int = 10,
+        filter_length_min: int = -1,
+        filter_length_max: float = -1,
+        lfr_m: int = 7,
+        lfr_n: int = 6,
+        dither: float = 1.0,
+    ) -> None:
+
+        self.fs = fs
+        self.window = window
+        self.n_mels = n_mels
+        self.frame_length = frame_length
+        self.frame_shift = frame_shift
+        self.filter_length_min = filter_length_min
+        self.filter_length_max = filter_length_max
+        self.lfr_m = lfr_m
+        self.lfr_n = lfr_n
+        self.lfr = LFR(lfr_m, lfr_n)
+        self.cmvn_file = cmvn_file
+        self.dither = dither
+
+        if self.cmvn_file:
+            self.cmvn = self.load_cmvn()
+
+    def apply_cmvn_batch(self, inputs: np.ndarray) -> np.ndarray:
+        """
+        Apply CMVN with mvn data
+        """
+        batch, frame, dim = inputs.shape
+        means = np.tile(self.cmvn[0:1, :dim], (frame, 1))
+        vars = np.tile(self.cmvn[1:2, :dim], (frame, 1))
+
+        means = torch.from_numpy(means).to(inputs.device)
+        vars = torch.from_numpy(vars).to(inputs.device)
+
+        inputs = (inputs + means) * vars
+        return inputs
+
+    def load_cmvn(
+        self,
+    ) -> np.ndarray:
+        with open(self.cmvn_file, "r", encoding="utf-8") as f:
+            lines = f.readlines()
+
+        means_list = []
+        vars_list = []
+        for i in range(len(lines)):
+            line_item = lines[i].split()
+            if line_item[0] == "<AddShift>":
+                line_item = lines[i + 1].split()
+                if line_item[0] == "<LearnRateCoef>":
+                    add_shift_line = line_item[3 : (len(line_item) - 1)]
+                    means_list = list(add_shift_line)
+                    continue
+            elif line_item[0] == "<Rescale>":
+                line_item = lines[i + 1].split()
+                if line_item[0] == "<LearnRateCoef>":
+                    rescale_line = line_item[3 : (len(line_item) - 1)]
+                    vars_list = list(rescale_line)
+                    continue
+
+        means = np.array(means_list).astype(np.float64)
+        vars = np.array(vars_list).astype(np.float64)
+        cmvn = np.array([means, vars])
+        return cmvn
+
+
+class Fbank(torch.nn.Module):
+    def __init__(self, opts):
+        super(Fbank, self).__init__()
+        self.fbank = kaldifeat.Fbank(opts)
+
+    def forward(self, waves: List[torch.Tensor]):
+        return self.fbank(waves)
+
+
+class TritonPythonModel:
+    """Your Python model must use the same class name. Every Python model
+    that is created must have "TritonPythonModel" as the class name.
+    """
+
+    def initialize(self, args):
+        """`initialize` is called only once when the model is being loaded.
+        Implementing `initialize` function is optional. This function allows
+        the model to initialize any state associated with this model.
+
+        Parameters
+        ----------
+        args : dict
+          Both keys and values are strings. The dictionary keys and values are:
+          * model_config: A JSON string containing the model configuration
+          * model_instance_kind: A string containing model instance kind
+          * model_instance_device_id: A string containing model instance device ID
+          * model_repository: Model repository path
+          * model_version: Model version
+          * model_name: Model name
+        """
+        self.model_config = model_config = json.loads(args["model_config"])
+        self.max_batch_size = max(model_config["max_batch_size"], 1)
+        self.device = "cuda"
+
+        # Get OUTPUT0 configuration
+        output0_config = pb_utils.get_output_config_by_name(model_config, "speech")
+        # Convert Triton types to numpy types
+        output0_dtype = pb_utils.triton_string_to_numpy(output0_config["data_type"])
+
+        if output0_dtype == np.float32:
+            self.output0_dtype = torch.float32
+        else:
+            self.output0_dtype = torch.float16
+
+        # Get OUTPUT1 configuration
+        output1_config = pb_utils.get_output_config_by_name(model_config, "speech_lengths")
+        # Convert Triton types to numpy types
+        self.output1_dtype = pb_utils.triton_string_to_numpy(output1_config["data_type"])
+
+        params = self.model_config["parameters"]
+
+        for li in params.items():
+            key, value = li
+            value = value["string_value"]
+            if key == "config_path":
+                with open(str(value), "rb") as f:
+                    config = yaml.load(f, Loader=yaml.Loader)
+            if key == "cmvn_path":
+                cmvn_path = str(value)
+        config["frontend_conf"]["cmvn_file"] = cmvn_path
+
+        opts = kaldifeat.FbankOptions()
+        opts.frame_opts.dither = 1.0  # TODO: 0.0 or 1.0
+        opts.frame_opts.window_type = config["frontend_conf"]["window"]
+        opts.mel_opts.num_bins = int(config["frontend_conf"]["n_mels"])
+        opts.frame_opts.frame_shift_ms = float(config["frontend_conf"]["frame_shift"])
+        opts.frame_opts.frame_length_ms = float(config["frontend_conf"]["frame_length"])
+        opts.frame_opts.samp_freq = int(config["frontend_conf"]["fs"])
+        opts.device = torch.device(self.device)
+        self.opts = opts
+        self.feature_extractor = Fbank(self.opts)
+        self.feature_size = opts.mel_opts.num_bins
+
+        self.frontend = WavFrontend(**config["frontend_conf"])
+
+    def extract_feat(self, waveform_list: List[np.ndarray]) -> Tuple[np.ndarray, np.ndarray]:
+        feats, feats_len = [], []
+        wavs = []
+        for waveform in waveform_list:
+            wav = torch.from_numpy(waveform).float().squeeze().to(self.device)
+            wavs.append(wav)
+
+        features = self.feature_extractor(wavs)
+        features_len = [feature.shape[0] for feature in features]
+        speech = torch.zeros(
+            (len(features), max(features_len), self.opts.mel_opts.num_bins),
+            dtype=self.output0_dtype,
+            device=self.device,
+        )
+        for i, feature in enumerate(features):
+            speech[i, : int(features_len[i])] = feature
+        speech_lens = torch.tensor(features_len, dtype=torch.int64).to(self.device)
+
+        feats, feats_len = self.frontend.lfr(speech, speech_lens)
+        feats_len = feats_len.type(torch.int32)
+
+        feats = self.frontend.apply_cmvn_batch(feats)
+        feats = feats.type(self.output0_dtype)
+
+        return feats, feats_len
+
+    def execute(self, requests):
+        """`execute` must be implemented in every Python model. `execute`
+        function receives a list of pb_utils.InferenceRequest as the only
+        argument. This function is called when an inference is requested
+        for this model.
+
+        Parameters
+        ----------
+        requests : list
+          A list of pb_utils.InferenceRequest
+
+        Returns
+        -------
+        list
+          A list of pb_utils.InferenceResponse. The length of this list must
+          be the same as `requests`
+        """
+        batch_count = []
+        total_waves = []
+        batch_len = []
+        responses = []
+        for request in requests:
+
+            input0 = pb_utils.get_input_tensor_by_name(request, "wav")
+            input1 = pb_utils.get_input_tensor_by_name(request, "wav_lens")
+
+            cur_b_wav = input0.as_numpy() * (1 << 15)  # b x -1
+            # remove paddings, however, encoder may can't batch requests since different lengths.
+            # cur_b_wav = cur_b_wav[:, : int(input1.as_numpy()[0])]
+            batch_count.append(cur_b_wav.shape[0])
+
+            # convert the bx-1 numpy array into a 1x-1 list of arrays
+            cur_b_wav_list = [np.expand_dims(cur_b_wav[i], 0) for i in range(cur_b_wav.shape[0])]
+            total_waves.extend(cur_b_wav_list)
+
+        features, feats_len = self.extract_feat(total_waves)
+
+        i = 0
+        for batch in batch_count:
+            speech = features[i : i + batch]
+            speech_lengths = feats_len[i : i + batch].unsqueeze(1)
+
+            speech, speech_lengths = speech.cpu(), speech_lengths.cpu()
+
+            out0 = pb_utils.Tensor.from_dlpack("speech", to_dlpack(speech))
+            out1 = pb_utils.Tensor.from_dlpack("speech_lengths", to_dlpack(speech_lengths))
+            inference_response = pb_utils.InferenceResponse(output_tensors=[out0, out1])
+            responses.append(inference_response)
+            i += batch
+
+        return responses

runtime/triton_gpu/model_repo_sense_voice_small/feature_extractor/config.pbtxt

@@ -0,0 +1,81 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+
+name: "feature_extractor"
+backend: "python"
+max_batch_size: 16
+
+parameters [
+  {
+    key: "num_mel_bins",
+    value: { string_value: "80"}
+  },
+  {
+    key: "frame_shift_in_ms"
+    value: { string_value: "10"}
+  },
+  {
+    key: "frame_length_in_ms"
+    value: { string_value: "25"}
+  },
+  {
+    key: "sample_rate"
+    value: { string_value: "16000"}
+  },
+  {
+    key: "cmvn_path"
+    value: { string_value: "./model_repo_sense_voice_small/feature_extractor/am.mvn"}
+  },
+  {
+    key: "config_path"
+    value: { string_value: "./model_repo_sense_voice_small/feature_extractor/config.yaml"}
+  }
+
+]
+
+input [
+  {
+    name: "wav"
+    data_type: TYPE_FP32
+    dims: [-1]
+  },
+  {
+    name: "wav_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+  }
+]
+
+output [
+  {
+    name: "speech"
+    data_type: TYPE_FP32
+    dims: [-1, 560]  # 80
+  },
+  {
+    name: "speech_lengths"
+    data_type: TYPE_INT32
+    dims: [1]
+  }
+]
+
+dynamic_batching {
+  }
+
+instance_group [
+    {
+      count: 2
+      kind: KIND_GPU
+    }
+]

runtime/triton_gpu/model_repo_sense_voice_small/feature_extractor/config.yaml

@@ -0,0 +1,97 @@
+encoder: SenseVoiceEncoderSmall
+encoder_conf:
+    output_size: 512
+    attention_heads: 4
+    linear_units: 2048
+    num_blocks: 50
+    tp_blocks: 20
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: pe
+    pos_enc_class: SinusoidalPositionEncoder
+    normalize_before: true
+    kernel_size: 11
+    sanm_shfit: 0
+    selfattention_layer_type: sanm
+
+
+model: SenseVoiceSmall
+model_conf:
+    length_normalized_loss: true
+    sos: 1
+    eos: 2
+    ignore_id: -1
+
+tokenizer: SentencepiecesTokenizer
+tokenizer_conf:
+  bpemodel: null
+  unk_symbol: <unk>
+  split_with_space: true
+
+frontend: WavFrontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 7
+    lfr_n: 6
+    cmvn_file: null
+
+
+dataset: SenseVoiceCTCDataset
+dataset_conf:
+  index_ds: IndexDSJsonl
+  batch_sampler: EspnetStyleBatchSampler
+  data_split_num: 32
+  batch_type: token
+  batch_size: 14000
+  max_token_length: 2000
+  min_token_length: 60
+  max_source_length: 2000
+  min_source_length: 60
+  max_target_length: 200
+  min_target_length: 0
+  shuffle: true
+  num_workers: 4
+  sos: ${model_conf.sos}
+  eos: ${model_conf.eos}
+  IndexDSJsonl: IndexDSJsonl
+  retry: 20
+
+train_conf:
+  accum_grad: 1
+  grad_clip: 5
+  max_epoch: 20
+  keep_nbest_models: 10
+  avg_nbest_model: 10
+  log_interval: 100
+  resume: true
+  validate_interval: 10000
+  save_checkpoint_interval: 10000
+
+optim: adamw
+optim_conf:
+  lr: 0.00002
+scheduler: warmuplr
+scheduler_conf:
+  warmup_steps: 25000
+
+specaug: SpecAugLFR
+specaug_conf:
+    apply_time_warp: false
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    lfr_rate: 6
+    num_freq_mask: 1
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 12
+    num_time_mask: 1

runtime/triton_gpu/model_repo_sense_voice_small/scoring/1/model.py

@@ -0,0 +1,136 @@
+#!/bin/bash
+#
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+#
+# 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 triton_python_backend_utils as pb_utils
+import numpy as np
+import torch
+from torch.utils.dlpack import from_dlpack
+
+import json
+import os
+import yaml
+
+import sentencepiece as spm
+
+
+class TritonPythonModel:
+    """Your Python model must use the same class name. Every Python model
+    that is created must have "TritonPythonModel" as the class name.
+    """
+
+    def initialize(self, args):
+        """`initialize` is called only once when the model is being loaded.
+        Implementing `initialize` function is optional. This function allows
+        the model to initialize any state associated with this model.
+
+        Parameters
+        ----------
+        args : dict
+          Both keys and values are strings. The dictionary keys and values are:
+          * model_config: A JSON string containing the model configuration
+          * model_instance_kind: A string containing model instance kind
+          * model_instance_device_id: A string containing model instance device ID
+          * model_repository: Model repository path
+          * model_version: Model version
+          * model_name: Model name
+        """
+        self.model_config = model_config = json.loads(args["model_config"])
+        self.max_batch_size = max(model_config["max_batch_size"], 1)
+
+        # # Get OUTPUT0 configuration
+        output0_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT0")
+        # # Convert Triton types to numpy types
+        self.out0_dtype = pb_utils.triton_string_to_numpy(output0_config["data_type"])
+
+        self.init_tokenizer(self.model_config["parameters"])
+
+    def init_tokenizer(self, parameters):
+        for li in parameters.items():
+            key, value = li
+            value = value["string_value"]
+            if key == "tokenizer_path":
+                tokenizer_path = value
+                self.tokenizer = spm.SentencePieceProcessor()
+                self.tokenizer.Load(tokenizer_path)
+
+    def execute(self, requests):
+        """`execute` must be implemented in every Python model. `execute`
+        function receives a list of pb_utils.InferenceRequest as the only
+        argument. This function is called when an inference is requested
+        for this model.
+
+        Parameters
+        ----------
+        requests : list
+          A list of pb_utils.InferenceRequest
+
+        Returns
+        -------
+        list
+          A list of pb_utils.InferenceResponse. The length of this list must
+          be the same as `requests`
+        """
+        # Every Python backend must iterate through list of requests and create
+        # an instance of pb_utils.InferenceResponse class for each of them. You
+        # should avoid storing any of the input Tensors in the class attributes
+        # as they will be overridden in subsequent inference requests. You can
+        # make a copy of the underlying NumPy array and store it if it is
+        # required.
+
+        total_seq = 0
+        logits_list, batch_count = [], []
+
+        for request in requests:
+            # Perform inference on the request and append it to responses list...
+            in_0 = pb_utils.get_input_tensor_by_name(request, "ctc_logits")
+
+            logits = from_dlpack(in_0.to_dlpack())
+            logits_list.append(logits)
+
+            total_seq += logits.shape[0]
+            batch_count.append(logits.shape[0])
+
+        logits_batch = torch.cat(logits_list, dim=0)
+        yseq_batch = logits_batch.argmax(axis=-1)
+        yseq_batch = torch.unique_consecutive(yseq_batch, dim=-1)
+
+        yseq_batch = yseq_batch.tolist()
+
+        # Remove blank_id and EOS tokens
+        token_int_batch = [list(filter(lambda x: x not in (0, 2), yseq)) for yseq in yseq_batch]
+
+        hyps = []
+        for i, token_int in enumerate(token_int_batch):
+            hyp = self.tokenizer.DecodeIds(token_int)
+            hyps.append(hyp)
+
+        responses = []
+        i = 0
+        for batch in batch_count:
+            sents = np.array(hyps[i : i + batch])
+            out0 = pb_utils.Tensor("OUTPUT0", sents.astype(self.out0_dtype))
+            inference_response = pb_utils.InferenceResponse(output_tensors=[out0])
+            responses.append(inference_response)
+            i += batch
+
+        return responses
+
+    def finalize(self):
+        """`finalize` is called only once when the model is being unloaded.
+        Implementing `finalize` function is optional. This function allows
+        the model to perform any necessary clean ups before exit.
+        """
+        print("Cleaning up...")

runtime/triton_gpu/model_repo_sense_voice_small/scoring/chn_jpn_yue_eng_ko_spectok.bpe.model

@@ -0,0 +1 @@
+/mnt/samsung-t7/yuekai/asr/funaudiollm/SenseVoiceSmall/chn_jpn_yue_eng_ko_spectok.bpe.model

runtime/triton_gpu/model_repo_sense_voice_small/scoring/config.pbtxt

@@ -0,0 +1,59 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+
+name: "scoring"
+backend: "python"
+max_batch_size: 16
+
+parameters [
+  {
+    key: "tokenizer_path",
+    value: { string_value: "./model_repo_sense_voice_small/scoring/chn_jpn_yue_eng_ko_spectok.bpe.model"}
+  },
+  { key: "FORCE_CPU_ONLY_INPUT_TENSORS" 
+    value: {string_value:"no"}
+  }
+]
+
+
+input [
+  {
+    name: "ctc_logits"
+    data_type: TYPE_FP32
+    dims: [-1, 25055]
+  },
+  {
+    name: "encoder_out_lens"
+    data_type: TYPE_INT32
+    dims: [1]
+    reshape: { shape: [ ] }
+  }
+]
+
+output [
+  {
+    name: "OUTPUT0"
+    data_type: TYPE_STRING
+    dims: [1]
+  }
+]
+
+dynamic_batching {
+  }
+instance_group [
+    {
+      count: 2
+      kind: KIND_CPU
+    }
+  ]

runtime/triton_gpu/model_repo_sense_voice_small/sensevoice/config.pbtxt

@@ -0,0 +1,117 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+
+name: "sensevoice"
+platform: "ensemble"
+max_batch_size: 16
+
+input [
+  {
+    name: "WAV"
+    data_type: TYPE_FP32
+    dims: [-1]
+  },
+  {
+    name: "WAV_LENS"
+    data_type: TYPE_INT32
+    dims: [1]
+  },
+  {
+    name: "LANGUAGE"
+    data_type: TYPE_INT32
+    dims: [1]
+  },
+  {
+    name: "TEXT_NORM"
+    data_type: TYPE_INT32
+    dims: [1]
+  }
+]
+
+output [
+  {
+    name: "TRANSCRIPTS"
+    data_type: TYPE_STRING
+    dims: [1]
+  }
+]
+
+ensemble_scheduling {
+ step [
+   {
+    model_name: "feature_extractor"
+    model_version: -1
+    input_map {
+      key: "wav"
+      value: "WAV"
+    }
+    input_map {
+      key: "wav_lens"
+      value: "WAV_LENS"
+    }
+    output_map {
+      key: "speech"
+      value: "SPEECH"
+    }
+    output_map {
+      key: "speech_lengths"
+      value: "SPEECH_LENGTHS"
+    }
+   },
+   {
+    model_name: "encoder"
+    model_version: -1
+    input_map {
+      key: "speech"
+      value: "SPEECH"
+    }
+    input_map {
+      key: "speech_lengths"
+      value: "SPEECH_LENGTHS"
+    }
+    input_map {
+      key: "language"
+      value: "LANGUAGE"
+    }
+    input_map {
+      key: "textnorm"
+      value: "TEXT_NORM"
+    }
+    output_map {
+      key: "ctc_logits"
+      value: "ctc_logits"
+    }
+    output_map {
+      key: "encoder_out_lens"
+      value: "encoder_out_lens"
+    }
+  },
+  {
+      model_name: "scoring"
+      model_version: -1
+      input_map {
+          key: "ctc_logits"
+          value: "ctc_logits"
+      }
+      input_map {
+          key: "encoder_out_lens"
+          value: "encoder_out_lens"
+      }
+      output_map {
+          key: "OUTPUT0"
+          value: "TRANSCRIPTS"
+      }
+  }
+ ]
+}