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import time
import gradio as gr
import librosa
import numpy as np
# import soundfile as sf
from transformers import pipeline
TARGET_SAMPLE_RATE = 16_000
AUDIO_SECONDS_THRESHOLD = 2
pipe = pipeline("audio-classification", model="MIT/ast-finetuned-audioset-10-10-0.4593")
prediction = [{"score": 1, "label": "recording..."}]
def normalize_waveform(waveform, datatype=np.float32): # source datatype: np.int16
waveform = waveform.astype(dtype=datatype)
waveform /= 32768.0
return waveform
def streaming_recording_fn(stream, new_chunk):
global prediction
sr, y = new_chunk
y = normalize_waveform(y)
y = librosa.resample(y, orig_sr=sr, target_sr=TARGET_SAMPLE_RATE)
if stream is not None:
if (stream.shape[-1] / TARGET_SAMPLE_RATE) >= AUDIO_SECONDS_THRESHOLD:
prediction = pipe(stream)
file_name = f'./audio/{time.strftime("%Y%m%d_%H%M%S", time.localtime())}.wav'
# # sf.write(file_name, stream, TARGET_SAMPLE_RATE)
print(f"SAVE AUDIO: {file_name}")
print(f">>>>>>1\t{y.shape=}, {stream.shape=}\n\t{prediction[0]=}")
stream = None
else:
stream = np.concatenate([stream, y], axis=-1)
print(f">>>>>>2\t{y.shape=}, {stream.shape=}")
else:
stream = y
print(f">>>>>>3\t{y.shape=}, {stream.shape=}")
return stream, {i['label']: i['score'] for i in prediction}
def microphone_fn(waveform):
print('-' * 120)
print(f"{waveform=}")
sr, y = waveform
y = normalize_waveform(y)
y = librosa.resample(y, orig_sr=sr, target_sr=TARGET_SAMPLE_RATE)
result = pipe(y)
file_name = f'./audio/{time.strftime("%Y%m%d_%H%M%S", time.localtime())}.wav'
# sf.write(file_name, y, TARGET_SAMPLE_RATE)
return {i['label']: i['score'] for i in result}
def file_fn(waveform):
print('-' * 120)
print(f"{waveform=}")
sr, y = waveform
y = normalize_waveform(y)
y = librosa.resample(y, orig_sr=sr, target_sr=TARGET_SAMPLE_RATE)
result = pipe(y)
file_name = f'./audio/{time.strftime("%Y%m%d_%H%M%S", time.localtime())}.wav'
# sf.write(file_name, y, TARGET_SAMPLE_RATE)
return {i['label']: i['score'] for i in result}
streaming_demo = gr.Interface(
fn=streaming_recording_fn,
inputs=["state", gr.Audio(sources=["microphone"], streaming=True)],
outputs=["state", "label"],
live=True,
)
microphone_demo = gr.Interface(
fn=microphone_fn,
inputs=[gr.Audio(sources=["microphone"], type="numpy")],
outputs=["label"]
)
file_demo = gr.Interface(
fn=file_fn,
inputs=[gr.Audio(sources=["upload"], type="numpy")],
outputs=["label"]
)
with gr.Blocks() as example:
inputs = [gr.Audio(sources=["upload"], type="numpy")]
output = gr.Label()
examples = [
["audio/cantina.wav"],
["audio/cat.mp3"]
]
ex = gr.Examples(examples,
fn=file_fn, inputs=inputs, outputs=output,
run_on_click=True)
with gr.Blocks() as demo:
gr.TabbedInterface([file_demo, streaming_demo, microphone_demo, example],
["Audio file", "Streaming", "Microphone", "Example"])
if __name__ == "__main__":
demo.launch(share=True)
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