File size: 4,087 Bytes
3b6b22e 09d9587 6b31279 3b6b22e 6b31279 09d9587 6b31279 09d9587 6b31279 3b6b22e 09d9587 6b31279 09d9587 6b31279 09d9587 6b31279 3b6b22e 904ac8c 1b42ccf 09d9587 3b6b22e 6b31279 500c811 6b31279 3b6b22e 500c811 6b31279 3b6b22e 6b31279 3b6b22e 6b31279 09d9587 3b6b22e 6b31279 09d9587 3b6b22e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
#!/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.
# References:
# https://gradio.app/docs/#dropdown
import os
import time
from datetime import datetime
import gradio as gr
import torchaudio
from model import (
get_gigaspeech_pre_trained_model,
sample_rate,
get_wenetspeech_pre_trained_model,
)
models = {
"Chinese": get_wenetspeech_pre_trained_model(),
"English": get_gigaspeech_pre_trained_model(),
}
def convert_to_wav(in_filename: str) -> str:
"""Convert the input audio file to a wave file"""
out_filename = in_filename + ".wav"
print(f"Converting '{in_filename}' to '{out_filename}'")
_ = os.system(f"ffmpeg -hide_banner -i '{in_filename}' '{out_filename}'")
return out_filename
demo = gr.Blocks()
def process(in_filename: str, language: str) -> str:
print("in_filename", in_filename)
print("language", language)
filename = convert_to_wav(in_filename)
now = datetime.now()
date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
print(f"Started at {date_time}")
start = time.time()
wave, wave_sample_rate = torchaudio.load(filename)
if wave_sample_rate != sample_rate:
print(
f"Expected sample rate: {sample_rate}. Given: {wave_sample_rate}. "
f"Resampling to {sample_rate}."
)
wave = torchaudio.functional.resample(
wave,
orig_freq=wave_sample_rate,
new_freq=sample_rate,
)
wave = wave[0] # use only the first channel.
hyp = models[language].decode_waves([wave])[0]
date_time = now.strftime("%Y-%m-%d %H:%M:%S.%f")
end = time.time()
duration = wave.shape[0] / sample_rate
rtf = (end - start) / duration
print(f"Finished at {date_time} s. Elapsed: {end - start: .3f} s")
print(f"Duration {duration: .3f} s")
print(f"RTF {rtf: .3f}")
print("hyp")
print(hyp)
return hyp
with demo:
gr.Markdown("# Automatic Speech Recognition with Next-gen Kaldi")
language_choices = list(models.keys())
language = gr.inputs.Radio(
label="Language",
choices=language_choices,
)
with gr.Tabs():
with gr.TabItem("Upload from disk"):
uploaded_file = gr.inputs.Audio(
source="upload", # Choose between "microphone", "upload"
type="filepath",
optional=False,
label="Upload from disk",
)
upload_button = gr.Button("Submit for recognition")
uploaded_output = gr.outputs.Textbox(
label="Recognized speech from uploaded file"
)
with gr.TabItem("Record from microphone"):
microphone = gr.inputs.Audio(
source="microphone", # Choose between "microphone", "upload"
type="filepath",
optional=False,
label="Record from microphone",
)
recorded_output = gr.outputs.Textbox(
label="Recognized speech from recordings"
)
record_button = gr.Button("Submit for recordings")
upload_button.click(
process,
inputs=[uploaded_file, language],
outputs=uploaded_output,
)
record_button.click(
process,
inputs=[microphone, language],
outputs=recorded_output,
)
if __name__ == "__main__":
demo.launch()
|