Spaces:
Sleeping
Sleeping
DrishtiSharma
commited on
Commit
•
5da8d71
1
Parent(s):
ca5824e
Update app.py
Browse files
app.py
CHANGED
@@ -1,68 +1,69 @@
|
|
1 |
-
#
|
2 |
-
|
|
|
|
|
|
|
|
|
|
|
3 |
|
4 |
-
Automatically generated by Colaboratory.
|
5 |
|
6 |
-
|
7 |
-
https://colab.research.google.com/drive/1Lv3LjRH9bHwMhKsWvFcELMzKqmXd9UIb
|
8 |
-
"""
|
9 |
|
10 |
-
!pip install -q transformers
|
11 |
-
!pip install -q gradio
|
12 |
|
13 |
-
|
14 |
-
|
15 |
-
|
16 |
-
|
17 |
-
|
18 |
-
|
19 |
-
|
|
|
|
|
|
|
20 |
|
21 |
-
input_file = "/content/drive/MyDrive/AAAAUDIO/My Audio.wav"
|
22 |
|
23 |
-
tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
|
24 |
-
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
|
25 |
-
|
26 |
-
def load_data(input_file):
|
27 |
-
|
28 |
-
""" Function for resampling to ensure that the speech input is sampled at 16KHz.
|
29 |
-
"""
|
30 |
-
#read the file
|
31 |
-
speech, sample_rate = sf.read(input_file)
|
32 |
-
|
33 |
-
#make it 1-D
|
34 |
-
if len(speech.shape) > 1:
|
35 |
-
speech = speech[:,0] + speech[:,1]
|
36 |
-
|
37 |
-
#Resampling at 16KHz since wav2vec2-base-960h is pretrained and fine-tuned on speech audio sampled at 16 KHz.
|
38 |
-
if sample_rate !=16000:
|
39 |
-
speech = librosa.resample(speech, sample_rate,16000)
|
40 |
-
return speech
|
41 |
|
42 |
-
|
43 |
-
speech = load_data(input_file)
|
44 |
|
45 |
-
#Tokenize
|
46 |
-
input_values = tokenizer(speech, return_tensors="pt").input_values
|
47 |
|
48 |
-
|
49 |
-
|
|
|
50 |
|
51 |
-
#Take argmax
|
52 |
-
predicted_ids = torch.argmax(logits, dim=-1)
|
53 |
|
54 |
-
#Get the words from predicted word ids
|
55 |
-
transcription = tokenizer.decode(predicted_ids[0])
|
56 |
|
57 |
-
|
58 |
-
|
|
|
59 |
|
60 |
-
|
|
|
61 |
|
62 |
-
|
63 |
-
|
64 |
-
|
65 |
-
|
66 |
-
description = "asdfghnjmk",
|
67 |
-
examples = [["/content/drive/MyDrive/AAAAUDIO/My Audio.wav"]]).launch()
|
68 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#importing all the necessary packages
|
2 |
+
import torch
|
3 |
+
import transformers
|
4 |
+
import gradio as gr
|
5 |
+
from torchaudio.sox_effects import apply_effects_file
|
6 |
+
from termcolor import colored
|
7 |
+
from transformers import Wav2Vec2FeatureExtractor, UniSpeechSatForAudioFrameClassification
|
8 |
|
|
|
9 |
|
10 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
|
|
|
11 |
|
|
|
|
|
12 |
|
13 |
+
# Defines the effects to apply to the audio file
|
14 |
+
EFFECTS = [
|
15 |
+
['remix', '-'], # merge all the channels
|
16 |
+
["channels", "1"], #channel-->mono
|
17 |
+
["rate", "16000"], # resample to 16000 Hz
|
18 |
+
["gain", "-1.0"], #Attenuation -1 dB
|
19 |
+
["silence", "1", "0.1", "0.1%", "-1", "0.1", "0.1%"],
|
20 |
+
#['pad', '0', '1.5'], # add 1.5 seconds silence at the end
|
21 |
+
['trim', '0', '10'], # get the first 10 seconds
|
22 |
+
]
|
23 |
|
|
|
24 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
25 |
|
26 |
+
THRESHOLD = 0.85 #depends on dataset
|
|
|
27 |
|
|
|
|
|
28 |
|
29 |
+
model_name = "microsoft/unispeech-sat-base-sd"
|
30 |
+
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
|
31 |
+
model = UniSpeechSatForAudioFrameClassification.from_pretrained(model_name).to(device)
|
32 |
|
|
|
|
|
33 |
|
|
|
|
|
34 |
|
35 |
+
def fn(path):
|
36 |
+
#Applying the effects to the audio input file
|
37 |
+
wav, _ = apply_effects_file(path, EFFECTS)
|
38 |
|
39 |
+
#Extracting features
|
40 |
+
input = feature_extractor(wav.squeez(0), return_tensors="pt", sampling_rate=16000).input_values.to(device)
|
41 |
|
42 |
+
with torch.no_grad():
|
43 |
+
logits = model(input).logits
|
44 |
+
logits = logits.to(device)
|
45 |
+
probabilities = torch.sigmoid(logits[0])
|
|
|
|
|
46 |
|
47 |
+
# labels is a one-hot array of shape (num_frames, num_speakers)
|
48 |
+
labels = (probabilities > 0.5).long()
|
49 |
+
return labels
|
50 |
+
|
51 |
+
|
52 |
+
|
53 |
+
|
54 |
+
inputs = [
|
55 |
+
gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Speaker #1"),
|
56 |
+
]
|
57 |
+
|
58 |
+
output = gr.outputs.HTML(label="")
|
59 |
+
|
60 |
+
gr.Interface(
|
61 |
+
fn=fn,
|
62 |
+
inputs=inputs,
|
63 |
+
outputs=output,
|
64 |
+
title="Speaker diarization using UniSpeech-SAT and X-Vectors").launch(enable_queue=True)
|
65 |
+
|
66 |
+
|
67 |
+
|
68 |
+
|
69 |
+
|