Support GigaAM CTC models for Russian ASR (#1464)

See also https://github.com/salute-developers/GigaAM

scripts/nemo/GigaAM/test-onnx-ctc.py

@@ -0,0 +1,157 @@
+#!/usr/bin/env python3
+# Copyright      2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+# https://github.com/salute-developers/GigaAM
+
+import kaldi_native_fbank as knf
+import librosa
+import numpy as np
+import onnxruntime as ort
+import soundfile as sf
+import torch
+
+
+def create_fbank():
+    opts = knf.FbankOptions()
+    opts.frame_opts.dither = 0
+    opts.frame_opts.remove_dc_offset = False
+    opts.frame_opts.preemph_coeff = 0
+    opts.frame_opts.window_type = "hann"
+
+    # Even though GigaAM uses 400 for fft, here we use 512
+    # since kaldi-native-fbank only support fft for power of 2.
+    opts.frame_opts.round_to_power_of_two = True
+
+    opts.mel_opts.low_freq = 0
+    opts.mel_opts.high_freq = 8000
+    opts.mel_opts.num_bins = 64
+
+    fbank = knf.OnlineFbank(opts)
+    return fbank
+
+
+def compute_features(audio, fbank) -> np.ndarray:
+    """
+    Args:
+      audio: (num_samples,), np.float32
+      fbank: the fbank extractor
+    Returns:
+      features: (num_frames, feat_dim), np.float32
+    """
+    assert len(audio.shape) == 1, audio.shape
+    fbank.accept_waveform(16000, audio)
+    ans = []
+    processed = 0
+    while processed < fbank.num_frames_ready:
+        ans.append(np.array(fbank.get_frame(processed)))
+        processed += 1
+    ans = np.stack(ans)
+    return ans
+
+
+def display(sess):
+    print("==========Input==========")
+    for i in sess.get_inputs():
+        print(i)
+    print("==========Output==========")
+    for i in sess.get_outputs():
+        print(i)
+
+
+"""
+==========Input==========
+NodeArg(name='audio_signal', type='tensor(float)', shape=['audio_signal_dynamic_axes_1', 64, 'audio_signal_dynamic_axes_2'])
+NodeArg(name='length', type='tensor(int64)', shape=['length_dynamic_axes_1'])
+==========Output==========
+NodeArg(name='logprobs', type='tensor(float)', shape=['logprobs_dynamic_axes_1', 'logprobs_dynamic_axes_2', 34])
+"""
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 1
+
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+        display(self.model)
+
+    def __call__(self, x: np.ndarray):
+        # x: (T, C)
+        x = torch.from_numpy(x)
+        x = x.t().unsqueeze(0)
+        # x: [1, C, T]
+        x_lens = torch.tensor([x.shape[-1]], dtype=torch.int64)
+
+        log_probs = self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: x.numpy(),
+                self.model.get_inputs()[1].name: x_lens.numpy(),
+            },
+        )[0]
+        # [batch_size, T, dim]
+        return log_probs
+
+
+def main():
+    filename = "./model.int8.onnx"
+    tokens = "./tokens.txt"
+    wav = "./example.wav"
+
+    model = OnnxModel(filename)
+
+    id2token = dict()
+    with open(tokens, encoding="utf-8") as f:
+        for line in f:
+            fields = line.split()
+            if len(fields) == 1:
+                id2token[int(fields[0])] = " "
+            else:
+                t, idx = fields
+                id2token[int(idx)] = t
+
+    fbank = create_fbank()
+    audio, sample_rate = sf.read(wav, dtype="float32", always_2d=True)
+    audio = audio[:, 0]  # only use the first channel
+    if sample_rate != 16000:
+        audio = librosa.resample(
+            audio,
+            orig_sr=sample_rate,
+            target_sr=16000,
+        )
+        sample_rate = 16000
+
+    features = compute_features(audio, fbank)
+    print("features.shape", features.shape)
+
+    blank = len(id2token) - 1
+    prev = -1
+    ans = []
+    log_probs = model(features)
+    print("log_probs", log_probs.shape)
+    log_probs = torch.from_numpy(log_probs)[0]
+    ids = torch.argmax(log_probs, dim=1).tolist()
+    for i in ids:
+        if i != blank and i != prev:
+            ans.append(i)
+        prev = i
+
+    tokens = [id2token[i] for i in ans]
+
+    text = "".join(tokens)
+    print(wav)
+    print(text)
+
+
+if __name__ == "__main__":
+    main()