From b2519607766daac8d038931150a283192c3e5e08 Mon Sep 17 00:00:00 2001
From: Jael Gu <mengjia.gu@zilliz.com>
Date: Mon, 6 Feb 2023 19:09:32 +0800
Subject: [PATCH] Update
Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
---
README.md | 32 +++++++++-----------------------
panns.py | 7 ++++---
requirements.txt | 2 +-
3 files changed, 14 insertions(+), 27 deletions(-)
diff --git a/README.md b/README.md
index e9b0963..b6130ec 100644
--- a/README.md
+++ b/README.md
@@ -15,33 +15,19 @@ The pre-trained model used here is from the paper **PANNs: Large-Scale Pretraine
Predict labels and generate embeddings given the audio path "test.wav".
- *Write the pipeline in simplified style*:
+*Write a pipeline with explicit inputs/outputs name specifications:*
```python
-import towhee
-
-(
- towhee.glob('test.wav')
- .audio_decode.ffmpeg()
- .runas_op(func=lambda x:[y[0] for y in x])
- .audio_classification.panns()
- .show()
-)
-```
-
-*Write a same pipeline with explicit inputs/outputs name specifications:*
+from towhee.dc2 import pipe, ops, DataCollection
-```python
-import towhee
-
-(
- towhee.glob['path']('test.wav')
- .audio_decode.ffmpeg['path', 'frames']()
- .runas_op['frames', 'frames'](func=lambda x:[y[0] for y in x])
- .audio_classification.panns['frames', ('labels', 'scores', 'vec')]()
- .select['path', 'labels', 'scores', 'vec']()
- .show()
+p = (
+ pipe.input('path')
+ .map('path', 'frame', ops.audio_decode.ffmpeg())
+ .map('frame', ('labels', 'scores', 'vec'), ops.audio_classification.panns())
+ .output('path', 'labels', 'scores', 'vec')
)
+
+DataCollection(p('./test.wav')).show()
```
<img src="./result.png" width="800px"/>
diff --git a/panns.py b/panns.py
index 9f05061..07e8877 100644
--- a/panns.py
+++ b/panns.py
@@ -17,10 +17,10 @@ import warnings
import os
import numpy
-import resampy
from typing import List
import torch
+import torchaudio
from panns_inference import AudioTagging, labels
@@ -67,9 +67,10 @@ class Panns(NNOperator):
audio = self.int2float(audio).astype('float32')
if sr != self.sample_rate:
- audio = resampy.resample(audio, sr, self.sample_rate)
+ resampler = torchaudio.transforms.Resample(sr, self.sample_rate, dtype=audio.dtype)
+ audio = resampler(audio)
- audio = torch.from_numpy(audio)[None, :]
+ audio = audio[None, :]
clipwise_output, embedding = self.tagger.inference(audio)
sorted_indexes = numpy.argsort(clipwise_output[0])[::-1]
diff --git a/requirements.txt b/requirements.txt
index 8ac3f1f..2c3a4fe 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
panns_inference
-resampy
torch
+torchaudio
towhee>=0.7.0
\ No newline at end of file