add speechbrain lid example

examples/vad_and_speechbrain_lid/README.md

+# Voice Activity Detection for Keyword Spotting
+In this example we will use two artificial neural networks. After gathering a chunk of audio we will check if there it contains human speech. This process is called Voice Activity Detection (VAD). If a voice is detected we start accumulating audio in order to feed the Keyword Spotting System (KWS). The KWS may be exchanged with any other AI that feeds on speech.
+
+Parts of this code are heavily borrowed from:
+- [silero-vad](https://github.com/snakers4/silero-vad)
+- [speech-commands](https://github.com/douglas125/SpeechCmdRecognition)
+
+Please check out [this fork of speech-commands](https://github.com/bytosaur/SpeechCmdRecognition) to train on specific words.
+
+### Installing Python Requirements
+__Note__: We suggest using virtual environments for dealing with python code.
+```shell
+pip install -r requirements
+```
+
+### Configuration
+The following parameters may be useful to look at 
+```python
+vad_threshold = 0.8		# minimum confidence of the VAD to trigger KWS
+kws_threshold = 0.95	# minimum confidence of the KWS to detect a word
+kws_required_size = 4	# number of chunks -1 to feed into the KWS
+frame_duration_ms = 250 # chunks size for the VAD in milliseconds (250ms is min)
+```
+The sample rate should be kept at 16kHz for both neural networks. If higher sample rates are required for recordings consider using downsampling. 
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examples/vad_and_speechbrain_lid/main.py

+import io
+import numpy as np
+import pyaudio
+import scipy.io.wavfile as wav
+import threading
+from queue import Queue
+import time
+
+import torch
+torch.set_num_threads(1)
+import torchaudio
+torchaudio.set_audio_backend("soundfile")
+from utils import *
+
+from speechbrain.pretrained import EncoderClassifier
+
+
+# Configure
+vad_threshold = 0.8		# minimum confidence of the VAD to trigger lid
+lid_threshold = 0.8		# minimum confidence of the lid to detect a word
+lid_required_size = 20	# number of chunks -1 to feed into the lid
+frame_duration_ms = 250 # chunks size for the VAD in milliseconds (250ms is min)
+
+# Pyaudio
+FORMAT = pyaudio.paInt16
+CHANNELS = 1
+SAMPLE_RATE = 16000
+CHUNK = int(SAMPLE_RATE / 10)
+audio = pyaudio.PyAudio()
+chunk_size = int(SAMPLE_RATE * frame_duration_ms / 1000.0)
+
+#=== Silero VAD ===#
+model = torch.jit.load('vad_model.jit')
+def normalize(sound):
+	abs_max = np.abs(sound).max()
+	if abs_max > 0:
+		sound *= 1/abs_max
+	sound = sound.squeeze()  # depends on the use case
+	return sound
+
+#=== LID ===#
+classes = []
+
+# lid process thread
+def processAudio(config, q):
+	model = EncoderClassifier.from_hparams(
+		"speechbrain/lang-id-commonlanguage_ecapa"
+	)
+	while True:
+		try:
+			data = q.get()
+			data = [item for sublist in data for item in sublist]
+			data_tensor = torch.tensor(data, dtype=float)
+			print(data_tensor)
+			# data_tensor /= 32768.0
+			data_tensor = torch.unsqueeze(data_tensor, 0)
+
+			out = model.classify_batch(data_tensor)
+			print(out[3])
+			# out = lid_model(data_tensor)[0].numpy()
+			# index = tf.math.argmax(out).numpy()
+
+			# if out[index] >= lid_threshold:
+			# 	print(classes[index])
+
+			# if you want to see the data please uncomment
+			# wav.write('results/'+classes[index]+'.wav', SAMPLE_RATE, np.asarray(data))
+
+		except Exception as e:
+			print("Ooopsi: ", e)
+		q.task_done()
+
+# Threading
+queue = Queue()
+some_config = ''
+worker = threading.Thread(target=processAudio, args=(some_config, queue), daemon=True)
+worker.start()
+
+data = []
+audio_int16 = []
+nu_voice_chunks = 0
+got_voice = False
+
+# wait a bit for libraries to load
+print("Loading libraries...")
+print("This may take up to 10 seconds!")
+time.sleep(7)
+stream = audio.open(format=FORMAT,
+					channels=CHANNELS,
+					rate=SAMPLE_RATE,
+					input=True,
+					frames_per_buffer=CHUNK)
+
+print("Listening...")
+while True:
+
+	# keep the last chunk so nothing gets lost
+	last_chunk = audio_int16
+
+	# sample chunk, convert to float and normalize
+	audio_chunk = stream.read(chunk_size)
+	audio_int16 = np.frombuffer(audio_chunk, np.int16)
+	audio_float32 = audio_int16.astype('float32')
+	audio_float32_norm = normalize(audio_float32)
+	
+	# get the confidences
+	vad_outs = validate(model, torch.from_numpy(audio_float32_norm))[:,1]
+
+	# trigger if voice is detected
+	if vad_outs >= vad_threshold and not got_voice:
+		print("Voice detected! Recording...")
+		got_voice = True
+		data = []
+		data.append(last_chunk)
+	
+	# collect data and analyze
+	if got_voice:
+		if nu_voice_chunks < lid_required_size:
+			data.append(audio_int16)
+			nu_voice_chunks += 1
+		else:
+			got_voice = False
+			nu_voice_chunks = 0
+			queue.put(data)
+
+queue.join()
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examples/vad_and_speechbrain_lid/requirements.txt

+# Silero VAD
+numpy==1.19.5
+torch==1.8.1
+matplotlib==3.4.2
+torchaudio==0.8.1
+soundfile==0.10.3.post1
+pyaudio==0.2.11
+
+# KWS
+speechbrain
+