From 3547adb4fb8b8284afefb8413382592fcdfa0302 Mon Sep 17 00:00:00 2001
From: Daniel <znsoft@163.com>
Date: 星期二, 07 三月 2023 12:18:06 +0800
Subject: [PATCH] Merge branch 'alibaba-damo-academy:main' into main
---
funasr/version.txt | 2
/dev/null | 0
funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/config.pbtxt | 4
funasr/runtime/triton_gpu/Dockerfile/Dockerfile.server | 4
funasr/runtime/triton_gpu/README.md | 55 ++++-
funasr/runtime/triton_gpu/client/decode_manifest_triton.py | 541 +++++++++++++++++++++++++++++++++++++++++++++++++
docs/images/damo.png | 0
funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/1/model.py | 7
funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/config.pbtxt | 2
README.md | 4
funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/1/model.py | 20 +
11 files changed, 610 insertions(+), 29 deletions(-)
diff --git a/README.md b/README.md
index b0454fb..aca9b8d 100644
--- a/README.md
+++ b/README.md
@@ -77,8 +77,8 @@
## Contributors
-| <div align="left"><img src="docs/images/DeepScience.png" width="250"/> |
-|:---:|
+| <div align="left"><img src="docs/images/damo.png" width="180"/> | <img src="docs/images/DeepScience.png" width="200"/> </div> |
+|:---------------------------------------------------------------:|:-----------------------------------------------------------:|
## Acknowledge
diff --git a/docs/images/damo.png b/docs/images/damo.png
new file mode 100644
index 0000000..f4f7a89
--- /dev/null
+++ b/docs/images/damo.png
Binary files differ
diff --git a/funasr/runtime/triton_gpu/Dockerfile/Dockerfile.server b/funasr/runtime/triton_gpu/Dockerfile/Dockerfile.server
index 459195c..d03610c 100644
--- a/funasr/runtime/triton_gpu/Dockerfile/Dockerfile.server
+++ b/funasr/runtime/triton_gpu/Dockerfile/Dockerfile.server
@@ -10,8 +10,10 @@
cmake \
libsndfile1
+# -i https://pypi.tuna.tsinghua.edu.cn/simple
+RUN pip3 install torch torchaudio
RUN pip3 install kaldifeat pyyaml
# Dependency for client
-RUN pip3 install soundfile grpcio-tools tritonclient pyyaml
+RUN pip3 install soundfile grpcio-tools tritonclient
WORKDIR /workspace
diff --git a/funasr/runtime/triton_gpu/README.md b/funasr/runtime/triton_gpu/README.md
index daceb4e..48e889c 100644
--- a/funasr/runtime/triton_gpu/README.md
+++ b/funasr/runtime/triton_gpu/README.md
@@ -1,16 +1,21 @@
## Inference with Triton
### Steps:
-1. Refer here to [get model.onnx](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/export/README.md)
-
-2. Follow below instructions to using triton
+1. Prepare model repo files
```sh
-# using docker image Dockerfile/Dockerfile.server
-docker build . -f Dockerfile/Dockerfile.server -t triton-paraformer:23.01
-docker run -it --rm --name "paraformer_triton_server" --gpus all -v <path_host/funasr/runtime/>:/workspace --shm-size 1g --net host triton-paraformer:23.01
-# inside the docker container, prepare previous exported model.onnx
-mv <path_model.onnx> /workspace/triton_gpu/model_repo_paraformer_large_offline/encoder/1/
+git-lfs install
+git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git
+pretrained_model_dir=$(pwd)/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
+
+cp $pretrained_model_dir/am.mvn ./model_repo_paraformer_large_offline/feature_extractor/
+cp $pretrained_model_dir/config.yaml ./model_repo_paraformer_large_offline/feature_extractor/
+
+# Refer here to get model.onnx (https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/export/README.md)
+cp <exported_onnx_dir>/model.onnx ./model_repo_paraformer_large_offline/encoder/1/
+```
+Log of directory tree:
+```sh
model_repo_paraformer_large_offline/
|-- encoder
| |-- 1
@@ -20,6 +25,7 @@
| |-- 1
| | `-- model.py
| |-- config.pbtxt
+| |-- am.mvn
| `-- config.yaml
|-- infer_pipeline
| |-- 1
@@ -27,13 +33,19 @@
`-- scoring
|-- 1
| `-- model.py
- |-- config.pbtxt
- `-- token_list.pkl
+ `-- config.pbtxt
8 directories, 9 files
+```
+
+2. Follow below instructions to launch triton server
+```sh
+# using docker image Dockerfile/Dockerfile.server
+docker build . -f Dockerfile/Dockerfile.server -t triton-paraformer:23.01
+docker run -it --rm --name "paraformer_triton_server" --gpus all -v <path_host/model_repo_paraformer_large_offline>:/workspace/ --shm-size 1g --net host triton-paraformer:23.01
# launch the service
-tritonserver --model-repository ./model_repo_paraformer_large_offline \
+tritonserver --model-repository /workspace/model_repo_paraformer_large_offline \
--pinned-memory-pool-byte-size=512000000 \
--cuda-memory-pool-byte-size=0:1024000000
@@ -43,6 +55,27 @@
Benchmark [speech_paraformer](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) based on Aishell1 test set with a single V100, the total audio duration is 36108.919 seconds.
+```sh
+# For client container:
+docker run -it --rm --name "client_test" --net host --gpus all -v <path_host/triton_gpu/client>:/workpace/ soar97/triton-k2:22.12.1 # noqa
+# For aishell manifests:
+apt-get install git-lfs
+git-lfs install
+git clone https://huggingface.co/csukuangfj/aishell-test-dev-manifests
+sudo mkdir -p /root/fangjun/open-source/icefall-aishell/egs/aishell/ASR/download/aishell
+tar xf ./aishell-test-dev-manifests/data_aishell.tar.gz -C /root/fangjun/open-source/icefall-aishell/egs/aishell/ASR/download/aishell/ # noqa
+
+serveraddr=localhost
+manifest_path=/workspace/aishell-test-dev-manifests/data/fbank/aishell_cuts_test.jsonl.gz
+num_task=60
+python3 client/decode_manifest_triton.py \
+ --server-addr $serveraddr \
+ --compute-cer \
+ --model-name infer_pipeline \
+ --num-tasks $num_task \
+ --manifest-filename $manifest_path
+```
+
(Note: The service has been fully warm up.)
|concurrent-tasks | processing time(s) | RTF |
|----------|--------------------|------------|
diff --git a/funasr/runtime/triton_gpu/client/decode_manifest_triton.py b/funasr/runtime/triton_gpu/client/decode_manifest_triton.py
new file mode 100644
index 0000000..3a8d57f
--- /dev/null
+++ b/funasr/runtime/triton_gpu/client/decode_manifest_triton.py
@@ -0,0 +1,541 @@
+#!/usr/bin/env python3
+# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
+# 2023 Nvidia (authors: Yuekai Zhang)
+# See LICENSE for clarification regarding multiple authors
+#
+# 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.
+"""
+This script loads a manifest in lhotse format and sends it to the server
+for decoding, in parallel.
+
+Usage:
+# For offline wenet server
+./decode_manifest_triton.py \
+ --server-addr localhost \
+ --compute-cer \
+ --model-name attention_rescoring \
+ --num-tasks 300 \
+ --manifest-filename ./aishell-test-dev-manifests/data/fbank/aishell_cuts_test.jsonl.gz # noqa
+
+# For streaming wenet server
+./decode_manifest_triton.py \
+ --server-addr localhost \
+ --streaming \
+ --compute-cer \
+ --context 7 \
+ --model-name streaming_wenet \
+ --num-tasks 300 \
+ --manifest-filename ./aishell-test-dev-manifests/data/fbank/aishell_cuts_test.jsonl.gz # noqa
+
+# For simulate streaming mode wenet server
+./decode_manifest_triton.py \
+ --server-addr localhost \
+ --simulate-streaming \
+ --compute-cer \
+ --context 7 \
+ --model-name streaming_wenet \
+ --num-tasks 300 \
+ --manifest-filename ./aishell-test-dev-manifests/data/fbank/aishell_cuts_test.jsonl.gz # noqa
+
+# For test container:
+docker run -it --rm --name "wenet_client_test" --net host --gpus all soar97/triton-k2:22.12.1 # noqa
+
+# For aishell manifests:
+apt-get install git-lfs
+git-lfs install
+git clone https://huggingface.co/csukuangfj/aishell-test-dev-manifests
+sudo mkdir -p /root/fangjun/open-source/icefall-aishell/egs/aishell/ASR/download/aishell
+tar xf ./aishell-test-dev-manifests/data_aishell.tar.gz -C /root/fangjun/open-source/icefall-aishell/egs/aishell/ASR/download/aishell/ # noqa
+
+"""
+
+import argparse
+import asyncio
+import math
+import time
+import types
+from pathlib import Path
+import json
+import numpy as np
+import tritonclient
+import tritonclient.grpc.aio as grpcclient
+from lhotse import CutSet, load_manifest
+from tritonclient.utils import np_to_triton_dtype
+
+from icefall.utils import store_transcripts, write_error_stats
+
+DEFAULT_MANIFEST_FILENAME = "/mnt/samsung-t7/yuekai/aishell-test-dev-manifests/data/fbank/aishell_cuts_test.jsonl.gz" # noqa
+
+
+def get_args():
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter
+ )
+
+ parser.add_argument(
+ "--server-addr",
+ type=str,
+ default="localhost",
+ help="Address of the server",
+ )
+
+ parser.add_argument(
+ "--server-port",
+ type=int,
+ default=8001,
+ help="Port of the server",
+ )
+
+ parser.add_argument(
+ "--manifest-filename",
+ type=str,
+ default=DEFAULT_MANIFEST_FILENAME,
+ help="Path to the manifest for decoding",
+ )
+
+ parser.add_argument(
+ "--model-name",
+ type=str,
+ default="transducer",
+ help="triton model_repo module name to request",
+ )
+
+ parser.add_argument(
+ "--num-tasks",
+ type=int,
+ default=50,
+ help="Number of tasks to use for sending",
+ )
+
+ parser.add_argument(
+ "--log-interval",
+ type=int,
+ default=5,
+ help="Controls how frequently we print the log.",
+ )
+
+ parser.add_argument(
+ "--compute-cer",
+ action="store_true",
+ default=False,
+ help="""True to compute CER, e.g., for Chinese.
+ False to compute WER, e.g., for English words.
+ """,
+ )
+
+ parser.add_argument(
+ "--streaming",
+ action="store_true",
+ default=False,
+ help="""True for streaming ASR.
+ """,
+ )
+
+ parser.add_argument(
+ "--simulate-streaming",
+ action="store_true",
+ default=False,
+ help="""True for strictly simulate streaming ASR.
+ Threads will sleep to simulate the real speaking scene.
+ """,
+ )
+
+ parser.add_argument(
+ "--chunk_size",
+ type=int,
+ required=False,
+ default=16,
+ help="chunk size default is 16",
+ )
+
+ parser.add_argument(
+ "--context",
+ type=int,
+ required=False,
+ default=-1,
+ help="subsampling context for wenet",
+ )
+
+ parser.add_argument(
+ "--encoder_right_context",
+ type=int,
+ required=False,
+ default=2,
+ help="encoder right context",
+ )
+
+ parser.add_argument(
+ "--subsampling",
+ type=int,
+ required=False,
+ default=4,
+ help="subsampling rate",
+ )
+
+ parser.add_argument(
+ "--stats_file",
+ type=str,
+ required=False,
+ default="./stats.json",
+ help="output of stats anaylasis",
+ )
+
+ return parser.parse_args()
+
+
+async def send(
+ cuts: CutSet,
+ name: str,
+ triton_client: tritonclient.grpc.aio.InferenceServerClient,
+ protocol_client: types.ModuleType,
+ log_interval: int,
+ compute_cer: bool,
+ model_name: str,
+):
+ total_duration = 0.0
+ results = []
+
+ for i, c in enumerate(cuts):
+ if i % log_interval == 0:
+ print(f"{name}: {i}/{len(cuts)}")
+
+ waveform = c.load_audio().reshape(-1).astype(np.float32)
+ sample_rate = 16000
+
+ # padding to nearset 10 seconds
+ samples = np.zeros(
+ (
+ 1,
+ 10 * sample_rate * (int(len(waveform) / sample_rate // 10) + 1),
+ ),
+ dtype=np.float32,
+ )
+ samples[0, : len(waveform)] = waveform
+
+ lengths = np.array([[len(waveform)]], dtype=np.int32)
+
+ inputs = [
+ protocol_client.InferInput(
+ "WAV", samples.shape, np_to_triton_dtype(samples.dtype)
+ ),
+ protocol_client.InferInput(
+ "WAV_LENS", lengths.shape, np_to_triton_dtype(lengths.dtype)
+ ),
+ ]
+ inputs[0].set_data_from_numpy(samples)
+ inputs[1].set_data_from_numpy(lengths)
+ outputs = [protocol_client.InferRequestedOutput("TRANSCRIPTS")]
+ sequence_id = 10086 + i
+
+ response = await triton_client.infer(
+ model_name, inputs, request_id=str(sequence_id), outputs=outputs
+ )
+
+ decoding_results = response.as_numpy("TRANSCRIPTS")[0]
+ if type(decoding_results) == np.ndarray:
+ decoding_results = b" ".join(decoding_results).decode("utf-8")
+ else:
+ # For wenet
+ decoding_results = decoding_results.decode("utf-8")
+
+ total_duration += c.duration
+
+ if compute_cer:
+ ref = c.supervisions[0].text.split()
+ hyp = decoding_results.split()
+ ref = list("".join(ref))
+ hyp = list("".join(hyp))
+ results.append((c.id, ref, hyp))
+ else:
+ results.append(
+ (
+ c.id,
+ c.supervisions[0].text.split(),
+ decoding_results.split(),
+ )
+ ) # noqa
+
+ return total_duration, results
+
+
+async def send_streaming(
+ cuts: CutSet,
+ name: str,
+ triton_client: tritonclient.grpc.aio.InferenceServerClient,
+ protocol_client: types.ModuleType,
+ log_interval: int,
+ compute_cer: bool,
+ model_name: str,
+ first_chunk_in_secs: float,
+ other_chunk_in_secs: float,
+ task_index: int,
+ simulate_mode: bool = False,
+):
+ total_duration = 0.0
+ results = []
+ latency_data = []
+
+ for i, c in enumerate(cuts):
+ if i % log_interval == 0:
+ print(f"{name}: {i}/{len(cuts)}")
+
+ waveform = c.load_audio().reshape(-1).astype(np.float32)
+ sample_rate = 16000
+
+ wav_segs = []
+
+ j = 0
+ while j < len(waveform):
+ if j == 0:
+ stride = int(first_chunk_in_secs * sample_rate)
+ wav_segs.append(waveform[j : j + stride])
+ else:
+ stride = int(other_chunk_in_secs * sample_rate)
+ wav_segs.append(waveform[j : j + stride])
+ j += len(wav_segs[-1])
+
+ sequence_id = task_index + 10086
+
+ for idx, seg in enumerate(wav_segs):
+ chunk_len = len(seg)
+
+ if simulate_mode:
+ await asyncio.sleep(chunk_len / sample_rate)
+
+ chunk_start = time.time()
+ if idx == 0:
+ chunk_samples = int(first_chunk_in_secs * sample_rate)
+ expect_input = np.zeros((1, chunk_samples), dtype=np.float32)
+ else:
+ chunk_samples = int(other_chunk_in_secs * sample_rate)
+ expect_input = np.zeros((1, chunk_samples), dtype=np.float32)
+
+ expect_input[0][0:chunk_len] = seg
+ input0_data = expect_input
+ input1_data = np.array([[chunk_len]], dtype=np.int32)
+
+ inputs = [
+ protocol_client.InferInput(
+ "WAV",
+ input0_data.shape,
+ np_to_triton_dtype(input0_data.dtype),
+ ),
+ protocol_client.InferInput(
+ "WAV_LENS",
+ input1_data.shape,
+ np_to_triton_dtype(input1_data.dtype),
+ ),
+ ]
+
+ inputs[0].set_data_from_numpy(input0_data)
+ inputs[1].set_data_from_numpy(input1_data)
+
+ outputs = [protocol_client.InferRequestedOutput("TRANSCRIPTS")]
+ end = False
+ if idx == len(wav_segs) - 1:
+ end = True
+
+ response = await triton_client.infer(
+ model_name,
+ inputs,
+ outputs=outputs,
+ sequence_id=sequence_id,
+ sequence_start=idx == 0,
+ sequence_end=end,
+ )
+ idx += 1
+
+ decoding_results = response.as_numpy("TRANSCRIPTS")
+ if type(decoding_results) == np.ndarray:
+ decoding_results = b" ".join(decoding_results).decode("utf-8")
+ else:
+ # For wenet
+ decoding_results = response.as_numpy("TRANSCRIPTS")[0].decode(
+ "utf-8"
+ )
+ chunk_end = time.time() - chunk_start
+ latency_data.append((chunk_end, chunk_len / sample_rate))
+
+ total_duration += c.duration
+
+ if compute_cer:
+ ref = c.supervisions[0].text.split()
+ hyp = decoding_results.split()
+ ref = list("".join(ref))
+ hyp = list("".join(hyp))
+ results.append((c.id, ref, hyp))
+ else:
+ results.append(
+ (
+ c.id,
+ c.supervisions[0].text.split(),
+ decoding_results.split(),
+ )
+ ) # noqa
+
+ return total_duration, results, latency_data
+
+
+async def main():
+ args = get_args()
+ filename = args.manifest_filename
+ server_addr = args.server_addr
+ server_port = args.server_port
+ url = f"{server_addr}:{server_port}"
+ num_tasks = args.num_tasks
+ log_interval = args.log_interval
+ compute_cer = args.compute_cer
+
+ cuts = load_manifest(filename)
+ cuts_list = cuts.split(num_tasks)
+ tasks = []
+
+ triton_client = grpcclient.InferenceServerClient(url=url, verbose=False)
+ protocol_client = grpcclient
+
+ if args.streaming or args.simulate_streaming:
+ frame_shift_ms = 10
+ frame_length_ms = 25
+ add_frames = math.ceil(
+ (frame_length_ms - frame_shift_ms) / frame_shift_ms
+ )
+ # decode_window_length: input sequence length of streaming encoder
+ if args.context > 0:
+ # decode window length calculation for wenet
+ decode_window_length = (
+ args.chunk_size - 1
+ ) * args.subsampling + args.context
+ else:
+ # decode window length calculation for icefall
+ decode_window_length = (
+ args.chunk_size + 2 + args.encoder_right_context
+ ) * args.subsampling + 3
+
+ first_chunk_ms = (decode_window_length + add_frames) * frame_shift_ms
+
+ start_time = time.time()
+ for i in range(num_tasks):
+ if args.streaming:
+ assert not args.simulate_streaming
+ task = asyncio.create_task(
+ send_streaming(
+ cuts=cuts_list[i],
+ name=f"task-{i}",
+ triton_client=triton_client,
+ protocol_client=protocol_client,
+ log_interval=log_interval,
+ compute_cer=compute_cer,
+ model_name=args.model_name,
+ first_chunk_in_secs=first_chunk_ms / 1000,
+ other_chunk_in_secs=args.chunk_size
+ * args.subsampling
+ * frame_shift_ms
+ / 1000,
+ task_index=i,
+ )
+ )
+ elif args.simulate_streaming:
+ task = asyncio.create_task(
+ send_streaming(
+ cuts=cuts_list[i],
+ name=f"task-{i}",
+ triton_client=triton_client,
+ protocol_client=protocol_client,
+ log_interval=log_interval,
+ compute_cer=compute_cer,
+ model_name=args.model_name,
+ first_chunk_in_secs=first_chunk_ms / 1000,
+ other_chunk_in_secs=args.chunk_size
+ * args.subsampling
+ * frame_shift_ms
+ / 1000,
+ task_index=i,
+ simulate_mode=True,
+ )
+ )
+ else:
+ task = asyncio.create_task(
+ send(
+ cuts=cuts_list[i],
+ name=f"task-{i}",
+ triton_client=triton_client,
+ protocol_client=protocol_client,
+ log_interval=log_interval,
+ compute_cer=compute_cer,
+ model_name=args.model_name,
+ )
+ )
+ tasks.append(task)
+
+ ans_list = await asyncio.gather(*tasks)
+
+ end_time = time.time()
+ elapsed = end_time - start_time
+
+ results = []
+ total_duration = 0.0
+ latency_data = []
+ for ans in ans_list:
+ total_duration += ans[0]
+ results += ans[1]
+ if args.streaming or args.simulate_streaming:
+ latency_data += ans[2]
+
+ rtf = elapsed / total_duration
+
+ s = f"RTF: {rtf:.4f}\n"
+ s += f"total_duration: {total_duration:.3f} seconds\n"
+ s += f"({total_duration/3600:.2f} hours)\n"
+ s += (
+ f"processing time: {elapsed:.3f} seconds "
+ f"({elapsed/3600:.2f} hours)\n"
+ )
+
+ if args.streaming or args.simulate_streaming:
+ latency_list = [
+ chunk_end for (chunk_end, chunk_duration) in latency_data
+ ]
+ latency_ms = sum(latency_list) / float(len(latency_list)) * 1000.0
+ latency_variance = np.var(latency_list, dtype=np.float64) * 1000.0
+ s += f"latency_variance: {latency_variance:.2f}\n"
+ s += f"latency_50_percentile: {np.percentile(latency_list, 50) * 1000.0:.2f}\n"
+ s += f"latency_90_percentile: {np.percentile(latency_list, 90) * 1000.0:.2f}\n"
+ s += f"latency_99_percentile: {np.percentile(latency_list, 99) * 1000.0:.2f}\n"
+ s += f"average_latency_ms: {latency_ms:.2f}\n"
+
+ print(s)
+
+ with open("rtf.txt", "w") as f:
+ f.write(s)
+
+ name = Path(filename).stem.split(".")[0]
+ results = sorted(results)
+ store_transcripts(filename=f"recogs-{name}.txt", texts=results)
+
+ with open(f"errs-{name}.txt", "w") as f:
+ write_error_stats(f, "test-set", results, enable_log=True)
+
+ with open(f"errs-{name}.txt", "r") as f:
+ print(f.readline()) # WER
+ print(f.readline()) # Detailed errors
+
+ if args.stats_file:
+ stats = await triton_client.get_inference_statistics(
+ model_name="", as_json=True
+ )
+ with open(args.stats_file, "w") as f:
+ json.dump(stats, f)
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
diff --git a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/1/model.py b/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/1/model.py
index 6464964..2f84bb8 100644
--- a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/1/model.py
+++ b/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/1/model.py
@@ -105,8 +105,8 @@
frame_shift: int = 10,
filter_length_min: int = -1,
filter_length_max: float = -1,
- lfr_m: int = 1,
- lfr_n: int = 1,
+ lfr_m: int = 7,
+ lfr_n: int = 6,
dither: float = 1.0
) -> None:
# check_argument_types()
@@ -229,22 +229,24 @@
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)
opts = kaldifeat.FbankOptions()
opts.frame_opts.dither = 1.0 # TODO: 0.0 or 1.0
- opts.frame_opts.window_type = config['WavFrontend']['frontend_conf']['window']
- opts.mel_opts.num_bins = int(config['WavFrontend']['frontend_conf']['n_mels'])
- opts.frame_opts.frame_shift_ms = float(config['WavFrontend']['frontend_conf']['frame_shift'])
- opts.frame_opts.frame_length_ms = float(config['WavFrontend']['frontend_conf']['frame_length'])
- opts.frame_opts.samp_freq = int(config['WavFrontend']['frontend_conf']['fs'])
+ 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(
- cmvn_file=config['WavFrontend']['cmvn_file'],
- **config['WavFrontend']['frontend_conf'])
+ cmvn_file=cmvn_path,
+ **config['frontend_conf'])
def extract_feat(self,
waveform_list: List[np.ndarray]
diff --git a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/config.pbtxt b/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/config.pbtxt
index 8b53183..44bfcd4 100644
--- a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/config.pbtxt
+++ b/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/config.pbtxt
@@ -34,6 +34,10 @@
value: { string_value: "16000"}
},
{
+ key: "cmvn_path"
+ value: { string_value: "./model_repo_paraformer_large_offline/feature_extractor/am.mvn"}
+ },
+ {
key: "config_path"
value: { string_value: "./model_repo_paraformer_large_offline/feature_extractor/config.yaml"}
}
diff --git a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/config.yaml b/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/config.yaml
deleted file mode 100644
index a4a66c3..0000000
--- a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/feature_extractor/config.yaml
+++ /dev/null
@@ -1,30 +0,0 @@
-TokenIDConverter:
- token_path: resources/models/token_list.pkl
- unk_symbol: <unk>
-
-CharTokenizer:
- symbol_value:
- space_symbol: <space>
- remove_non_linguistic_symbols: false
-
-WavFrontend:
- cmvn_file: /raid/dgxsa/yuekaiz/pull_requests/FunASR/funasr/runtime/python/onnxruntime/resources/models/am.mvn
- frontend_conf:
- fs: 16000
- window: hamming
- n_mels: 80
- frame_length: 25
- frame_shift: 10
- lfr_m: 7
- lfr_n: 6
- filter_length_max: -.inf
-
-Model:
- model_path: resources/models/model.onnx
- use_cuda: false
- CUDAExecutionProvider:
- device_id: 0
- arena_extend_strategy: kNextPowerOfTwo
- cudnn_conv_algo_search: EXHAUSTIVE
- do_copy_in_default_stream: true
- batch_size: 3
\ No newline at end of file
diff --git a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/1/model.py b/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/1/model.py
index dfbaa52..ef6278d 100644
--- a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/1/model.py
+++ b/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/1/model.py
@@ -21,8 +21,7 @@
import json
import os
-
-import pickle
+import yaml
class TritonPythonModel:
"""Your Python model must use the same class name. Every Python model
@@ -76,8 +75,8 @@
load lang_char.txt
"""
with open(str(vocab_file), 'rb') as f:
- token_list = pickle.load(f)
- return token_list
+ config = yaml.load(f, Loader=yaml.Loader)
+ return config['token_list']
def execute(self, requests):
"""`execute` must be implemented in every Python model. `execute`
diff --git a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/config.pbtxt b/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/config.pbtxt
index 6b43fe4..a63d1c5 100644
--- a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/config.pbtxt
+++ b/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/config.pbtxt
@@ -23,7 +23,7 @@
},
{
key: "vocabulary",
- value: { string_value: "./model_repo_paraformer_large_offline/scoring/token_list.pkl"}
+ value: { string_value: "./model_repo_paraformer_large_offline/feature_extractor/config.yaml"}
},
{
key: "lm_path"
diff --git a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/token_list.pkl b/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/token_list.pkl
deleted file mode 100644
index f1a2ce7..0000000
--- a/funasr/runtime/triton_gpu/model_repo_paraformer_large_offline/scoring/token_list.pkl
+++ /dev/null
Binary files differ
diff --git a/funasr/version.txt b/funasr/version.txt
index ee1372d..7179039 100644
--- a/funasr/version.txt
+++ b/funasr/version.txt
@@ -1 +1 @@
-0.2.2
+0.2.3
--
Gitblit v1.9.1