v4.0stable force_onnx_cpu fx

Adamnsandle

README.md

@@ -15,7 +15,7 @@ This repository also includes Number Detector and Language classifier [models](h
 <br/>
 
 <p align="center">
-  <img src="https://user-images.githubusercontent.com/36505480/145563071-681b57e3-06b5-4cd0-bdee-e2ade3d50a60.png" />
+  <img src="https://user-images.githubusercontent.com/36505480/198026365-8da383e0-5398-4a12-b7f8-22c2c0059512.png" />
 </p>
 
 <details>
@@ -29,17 +29,17 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-
 <h2 align="center">Key Features</h2>
 <br/>
 
-- **High accuracy**
+- **Stellar accuracy**
 
   Silero VAD has [excellent results](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#vs-other-available-solutions) on speech detection tasks.
   
 - **Fast**
 
-  One audio chunk (30+ ms) [takes](https://github.com/snakers4/silero-vad/wiki/Performance-Metrics#silero-vad-performance-metrics) around **1ms** to be processed on a single CPU thread. Using batching or GPU can also improve performance considerably.
+  One audio chunk (30+ ms) [takes](https://github.com/snakers4/silero-vad/wiki/Performance-Metrics#silero-vad-performance-metrics) less than **1ms** to be processed on a single CPU thread. Using batching or GPU can also improve performance considerably. Under certain conditions ONNX may even run up to 4-5x faster. 
 
 - **Lightweight**
 
-  JIT model is less than one megabyte in size.
+  JIT model is around one megabyte in size.
 
 - **General**
 
@@ -47,11 +47,19 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-
 
 - **Flexible sampling rate**
 
-  Silero VAD [supports](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#sample-rate-comparison)  **8000 Hz** and **16000 Hz** (JIT) and **16000 Hz** (ONNX) [sampling rates](https://en.wikipedia.org/wiki/Sampling_(signal_processing)#Sampling_rate).
+  Silero VAD [supports](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics#sample-rate-comparison)  **8000 Hz** and **16000 Hz** [sampling rates](https://en.wikipedia.org/wiki/Sampling_(signal_processing)#Sampling_rate).
 
 - **Flexible chunk size**
 
-  Model was trained on audio chunks of different lengths. **30 ms**, **60 ms** and **100 ms** long chunks are supported directly, others may work as well.
+  Model was trained on **30 ms**. Longer chunks are supported directly, others may work as well.
+
+- **Highly Portable**
+
+  Silero VAD reaps benefits from the rich ecosystems built around **PyTorch** and **ONNX** running everywhere where these runtimes are available.
+
+- **No Strings Attached**
+
+   Published under permissive license (MIT) Silero VAD has zero strings attached - no telemetry, no keys, no registration, no built-in expiration, no keys or vendor lock.
 
 <br/>
 <h2 align="center">Typical Use Cases</h2>
@@ -70,9 +78,10 @@ https://user-images.githubusercontent.com/36505480/144874384-95f80f6d-a4f1-42cc-
 - [Examples and Dependencies](https://github.com/snakers4/silero-vad/wiki/Examples-and-Dependencies#dependencies)
 - [Quality Metrics](https://github.com/snakers4/silero-vad/wiki/Quality-Metrics)
 - [Performance Metrics](https://github.com/snakers4/silero-vad/wiki/Performance-Metrics)
-- Number Detector and Language classifier [models](https://github.com/snakers4/silero-vad/wiki/Other-Models)
+- [Number Detector and Language classifier models](https://github.com/snakers4/silero-vad/wiki/Other-Models)
 - [Versions and Available Models](https://github.com/snakers4/silero-vad/wiki/Version-history-and-Available-Models)
 - [Further reading](https://github.com/snakers4/silero-models#further-reading)
+- [FAQ](https://github.com/snakers4/silero-vad/wiki/FAQ)
 
 <br/>
 <h2 align="center">Get In Touch</h2>
@@ -96,3 +105,9 @@ Please see our [wiki](https://github.com/snakers4/silero-models/wiki) and [tiers
   email = {hello@silero.ai}
 }
 ```
+
+<br/>
+<h2 align="center">VAD-based Community Apps</h2>
+<br/>
+
+- Voice activity detection for the [browser](https://github.com/ricky0123/vad) using ONNX Runtime Web 

hubconf.py

@@ -16,14 +16,25 @@ from utils_vad import (init_jit_model,
                        OnnxWrapper)
 
 
-def silero_vad(onnx=False):
+def versiontuple(v):
+    return tuple(map(int, (v.split('+')[0].split("."))))
+
+
+def silero_vad(onnx=False, force_onnx_cpu=False):
     """Silero Voice Activity Detector
     Returns a model with a set of utils
     Please see https://github.com/snakers4/silero-vad for usage examples
     """
+
+    if not onnx:
+        installed_version = torch.__version__
+        supported_version = '1.12.0'
+        if versiontuple(installed_version) < versiontuple(supported_version):
+            raise Exception(f'Please install torch {supported_version} or greater ({installed_version} installed)')
+
     model_dir = os.path.join(os.path.dirname(__file__), 'files')
     if onnx:
-        model = OnnxWrapper(os.path.join(model_dir, 'silero_vad.onnx'))
+        model = OnnxWrapper(os.path.join(model_dir, 'silero_vad.onnx'), force_onnx_cpu)
     else:
         model = init_jit_model(os.path.join(model_dir, 'silero_vad.jit'))
     utils = (get_speech_timestamps,
@@ -35,7 +46,7 @@ def silero_vad(onnx=False):
     return model, utils
 
 
-def silero_number_detector(onnx=False):
+def silero_number_detector(onnx=False, force_onnx_cpu=False):
     """Silero Number Detector
     Returns a model with a set of utils
     Please see https://github.com/snakers4/silero-vad for usage examples
@@ -44,7 +55,7 @@ def silero_number_detector(onnx=False):
         url = 'https://models.silero.ai/vad_models/number_detector.onnx'
     else:
         url = 'https://models.silero.ai/vad_models/number_detector.jit'
-    model = Validator(url)
+    model = Validator(url, force_onnx_cpu)
     utils = (get_number_ts,
              save_audio,
              read_audio,
@@ -54,7 +65,7 @@ def silero_number_detector(onnx=False):
     return model, utils
 
 
-def silero_lang_detector(onnx=False):
+def silero_lang_detector(onnx=False, force_onnx_cpu=False):
     """Silero Language Classifier
     Returns a model with a set of utils
     Please see https://github.com/snakers4/silero-vad for usage examples
@@ -63,14 +74,14 @@ def silero_lang_detector(onnx=False):
         url = 'https://models.silero.ai/vad_models/number_detector.onnx'
     else:
         url = 'https://models.silero.ai/vad_models/number_detector.jit'
-    model = Validator(url)
+    model = Validator(url, force_onnx_cpu)
     utils = (get_language,
              read_audio)
 
     return model, utils
 
 
-def silero_lang_detector_95(onnx=False):
+def silero_lang_detector_95(onnx=False, force_onnx_cpu=False):
     """Silero Language Classifier (95 languages)
     Returns a model with a set of utils
     Please see https://github.com/snakers4/silero-vad for usage examples
@@ -80,8 +91,8 @@ def silero_lang_detector_95(onnx=False):
         url = 'https://models.silero.ai/vad_models/lang_classifier_95.onnx'
     else:
         url = 'https://models.silero.ai/vad_models/lang_classifier_95.jit'
-    model = Validator(url)
-    
+    model = Validator(url, force_onnx_cpu)
+
     model_dir = os.path.join(os.path.dirname(__file__), 'files')
     with open(os.path.join(model_dir, 'lang_dict_95.json'), 'r') as f:
         lang_dict = json.load(f)

silero-vad.ipynb

@@ -138,7 +138,10 @@
     "\n",
     "window_size_samples = 1536 # number of samples in a single audio chunk\n",
     "for i in range(0, len(wav), window_size_samples):\n",
-    "    speech_dict = vad_iterator(wav[i: i+ window_size_samples], return_seconds=True)\n",
+    "    chunk = wav[i: i+ window_size_samples]\n",
+    "    if len(chunk) < window_size_samples:\n",
+    "      break\n",
+    "    speech_dict = vad_iterator(chunk, return_seconds=True)\n",
     "    if speech_dict:\n",
     "        print(speech_dict, end=' ')\n",
     "vad_iterator.reset_states() # reset model states after each audio"
@@ -158,7 +161,10 @@
     "speech_probs = []\n",
     "window_size_samples = 1536\n",
     "for i in range(0, len(wav), window_size_samples):\n",
-    "    speech_prob = model(wav[i: i+ window_size_samples], SAMPLING_RATE).item()\n",
+    "    chunk = wav[i: i+ window_size_samples]\n",
+    "    if len(chunk) < window_size_samples:\n",
+    "      break\n",
+    "    speech_prob = model(chunk, SAMPLING_RATE).item()\n",
     "    speech_probs.append(speech_prob)\n",
     "vad_iterator.reset_states() # reset model states after each audio\n",
     "\n",

utils_vad.py

@@ -9,51 +9,98 @@ languages = ['ru', 'en', 'de', 'es']
 
 class OnnxWrapper():
 
-    def __init__(self, path):
+    def __init__(self, path, force_onnx_cpu=False):
         import numpy as np
         global np
         import onnxruntime
-        self.session = onnxruntime.InferenceSession(path)
+        if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
+            self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'])
+        else:
+            self.session = onnxruntime.InferenceSession(path)
         self.session.intra_op_num_threads = 1
         self.session.inter_op_num_threads = 1
 
         self.reset_states()
+        self.sample_rates = [8000, 16000]
 
-    def reset_states(self):
-        self._h = np.zeros((2, 1, 64)).astype('float32')
-        self._c = np.zeros((2, 1, 64)).astype('float32')
-
-    def __call__(self, x, sr: int):
+    def _validate_input(self, x, sr: int):
         if x.dim() == 1:
             x = x.unsqueeze(0)
         if x.dim() > 2:
             raise ValueError(f"Too many dimensions for input audio chunk {x.dim()}")
 
-        if x.shape[0] > 1:
-            raise ValueError("Onnx model does not support batching")
+        if sr != 16000 and (sr % 16000 == 0):
+            step = sr // 16000
+            x = x[::step]
+            sr = 16000
 
-        if sr not in [16000]:
-            raise ValueError(f"Supported sample rates: {[16000]}")
+        if sr not in self.sample_rates:
+            raise ValueError(f"Supported sampling rates: {self.sample_rates} (or multiply of 16000)")
 
         if sr / x.shape[1] > 31.25:
             raise ValueError("Input audio chunk is too short")
 
-        ort_inputs = {'input': x.numpy(), 'h0': self._h, 'c0': self._c}
-        ort_outs = self.session.run(None, ort_inputs)
-        out, self._h, self._c = ort_outs
+        return x, sr
 
-        out = torch.tensor(out).squeeze(2)[:, 1]  # make output type match JIT analog
+    def reset_states(self, batch_size=1):
+        self._h = np.zeros((2, batch_size, 64)).astype('float32')
+        self._c = np.zeros((2, batch_size, 64)).astype('float32')
+        self._last_sr = 0
+        self._last_batch_size = 0
 
+    def __call__(self, x, sr: int):
+
+        x, sr = self._validate_input(x, sr)
+        batch_size = x.shape[0]
+
+        if not self._last_batch_size:
+            self.reset_states(batch_size)
+        if (self._last_sr) and (self._last_sr != sr):
+            self.reset_states(batch_size)
+        if (self._last_batch_size) and (self._last_batch_size != batch_size):
+            self.reset_states(batch_size)
+
+        if sr in [8000, 16000]:
+            ort_inputs = {'input': x.numpy(), 'h': self._h, 'c': self._c, 'sr': np.array(sr)}
+            ort_outs = self.session.run(None, ort_inputs)
+            out, self._h, self._c = ort_outs
+        else:
+            raise ValueError()
+
+        self._last_sr = sr
+        self._last_batch_size = batch_size
+
+        out = torch.tensor(out)
         return out
 
+    def audio_forward(self, x, sr: int, num_samples: int = 512):
+        outs = []
+        x, sr = self._validate_input(x, sr)
+
+        if x.shape[1] % num_samples:
+            pad_num = num_samples - (x.shape[1] % num_samples)
+            x = torch.nn.functional.pad(x, (0, pad_num), 'constant', value=0.0)
+
+        self.reset_states(x.shape[0])
+        for i in range(0, x.shape[1], num_samples):
+            wavs_batch = x[:, i:i+num_samples]
+            out_chunk = self.__call__(wavs_batch, sr)
+            outs.append(out_chunk)
+
+        stacked = torch.cat(outs, dim=1)
+        return stacked.cpu()
+
 
 class Validator():
-    def __init__(self, url):
+    def __init__(self, url, force_onnx_cpu):
         self.onnx = True if url.endswith('.onnx') else False
         torch.hub.download_url_to_file(url, 'inf.model')
         if self.onnx:
             import onnxruntime
-            self.model = onnxruntime.InferenceSession('inf.model')
+            if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
+                self.model = onnxruntime.InferenceSession('inf.model', providers=['CPUExecutionProvider'])
+            else:
+                self.model = onnxruntime.InferenceSession('inf.model')
         else:
             self.model = init_jit_model(model_path='inf.model')
 
@@ -117,7 +164,7 @@ def get_speech_timestamps(audio: torch.Tensor,
                           sampling_rate: int = 16000,
                           min_speech_duration_ms: int = 250,
                           min_silence_duration_ms: int = 100,
-                          window_size_samples: int = 1536,
+                          window_size_samples: int = 512,
                           speech_pad_ms: int = 30,
                           return_seconds: bool = False,
                           visualize_probs: bool = False):
@@ -177,8 +224,16 @@ def get_speech_timestamps(audio: torch.Tensor,
         if len(audio.shape) > 1:
             raise ValueError("More than one dimension in audio. Are you trying to process audio with 2 channels?")
 
+    if sampling_rate > 16000 and (sampling_rate % 16000 == 0):
+        step = sampling_rate // 16000
+        sampling_rate = 16000
+        audio = audio[::step]
+        warnings.warn('Sampling rate is a multiply of 16000, casting to 16000 manually!')
+    else:
+        step = 1
+
     if sampling_rate == 8000 and window_size_samples > 768:
-        warnings.warn('window_size_samples is too big for 8000 sampling_rate! Better set window_size_samples to 256, 512 or 1536 for 8000 sample rate!')
+        warnings.warn('window_size_samples is too big for 8000 sampling_rate! Better set window_size_samples to 256, 512 or 768 for 8000 sample rate!')
     if window_size_samples not in [256, 512, 768, 1024, 1536]:
         warnings.warn('Unusual window_size_samples! Supported window_size_samples:\n - [512, 1024, 1536] for 16000 sampling_rate\n - [256, 512, 768] for 8000 sampling_rate')
 
@@ -226,7 +281,7 @@ def get_speech_timestamps(audio: torch.Tensor,
                 triggered = False
                 continue
 
-    if current_speech:
+    if current_speech and (audio_length_samples - current_speech['start']) > min_speech_samples:
         current_speech['end'] = audio_length_samples
         speeches.append(current_speech)
 
@@ -239,7 +294,8 @@ def get_speech_timestamps(audio: torch.Tensor,
                 speech['end'] += int(silence_duration // 2)
                 speeches[i+1]['start'] = int(max(0, speeches[i+1]['start'] - silence_duration // 2))
             else:
-                speech['end'] += int(speech_pad_samples)
+                speech['end'] = int(min(audio_length_samples, speech['end'] + speech_pad_samples))
+                speeches[i+1]['start'] = int(max(0, speeches[i+1]['start'] - speech_pad_samples))
         else:
             speech['end'] = int(min(audio_length_samples, speech['end'] + speech_pad_samples))
 
@@ -247,6 +303,10 @@ def get_speech_timestamps(audio: torch.Tensor,
         for speech_dict in speeches:
             speech_dict['start'] = round(speech_dict['start'] / sampling_rate, 1)
             speech_dict['end'] = round(speech_dict['end'] / sampling_rate, 1)
+    elif step > 1:
+        for speech_dict in speeches:
+            speech_dict['start'] *= step
+            speech_dict['end'] *= step
 
     if visualize_probs:
         make_visualization(speech_probs, window_size_samples / sampling_rate)
@@ -353,6 +413,10 @@ class VADIterator:
         self.model = model
         self.threshold = threshold
         self.sampling_rate = sampling_rate
+
+        if sampling_rate not in [8000, 16000]:
+            raise ValueError('VADIterator does not support sampling rates other than [8000, 16000]')
+
         self.min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
         self.speech_pad_samples = sampling_rate * speech_pad_ms / 1000
         self.reset_states()