mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-09-07 16:14:17 +00:00
58 lines
2.3 KiB
Python
Executable File
58 lines
2.3 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
|
|
#
|
|
# 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.
|
|
|
|
"""
|
|
A client for offline ASR recognition.
|
|
"""
|
|
import torch
|
|
import torchaudio
|
|
import websockets
|
|
import asyncio
|
|
|
|
|
|
async def main():
|
|
test_wavs = [
|
|
"/ceph-fj/fangjun/open-source-2/icefall-models/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/test_wavs/1089-134686-0001.wav",
|
|
"/ceph-fj/fangjun/open-source-2/icefall-models/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/test_wavs/1221-135766-0001.wav",
|
|
"/ceph-fj/fangjun/open-source-2/icefall-models/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/test_wavs/1221-135766-0002.wav",
|
|
]
|
|
async with websockets.connect("ws://localhost:6006") as websocket:
|
|
while True:
|
|
for test_wav in test_wavs:
|
|
print(f"Sending {test_wav}")
|
|
wave, sample_rate = torchaudio.load(test_wav)
|
|
wave = wave.squeeze(0)
|
|
num_bytes = wave.numel() * wave.element_size()
|
|
print(f"Sending {num_bytes}, {wave.shape}")
|
|
await websocket.send(
|
|
(num_bytes).to_bytes(8, "big", signed=True)
|
|
)
|
|
|
|
frame_size = 1048576 // 4 # max payload is 1MB
|
|
num_sent_samples = 0
|
|
start = 0
|
|
while start < wave.numel():
|
|
end = start + frame_size
|
|
await websocket.send(wave.numpy().data[start:end])
|
|
start = end
|
|
decoding_results = await websocket.recv()
|
|
print(decoding_results)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|