Export NeMo FastConformer Hybrid Transducer-CTC Large Streaming to ONNX. (#843)

scripts/nemo/fast-conformer-hybrid-transducer-ctc/test-onnx-ctc.py

@@ -0,0 +1,197 @@
+#!/usr/bin/env python3
+
+import argparse
+from pathlib import Path
+
+import kaldi_native_fbank as knf
+import numpy as np
+import onnxruntime as ort
+import torch
+import soundfile as sf
+import librosa
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", type=str, required=True, help="Path to model.onnx")
+
+    parser.add_argument("--tokens", type=str, required=True, help="Path to tokens.txt")
+
+    parser.add_argument("--wav", type=str, required=True, help="Path to test.wav")
+
+    return parser.parse_args()
+
+
+def create_fbank():
+    opts = knf.FbankOptions()
+    opts.frame_opts.dither = 0
+    opts.frame_opts.remove_dc_offset = False
+    opts.frame_opts.window_type = "hann"
+
+    opts.mel_opts.low_freq = 0
+    opts.mel_opts.num_bins = 80
+
+    opts.mel_opts.is_librosa = True
+
+    fbank = knf.OnlineFbank(opts)
+    return fbank
+
+
+def compute_features(audio, fbank):
+    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
+
+
+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.session_opts = session_opts
+
+        self.model = ort.InferenceSession(
+            filename,
+            sess_options=self.session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+
+        meta = self.model.get_modelmeta().custom_metadata_map
+        print(meta)
+
+        self.window_size = int(meta["window_size"])
+        self.chunk_shift = int(meta["chunk_shift"])
+
+        self.cache_last_channel_dim1 = int(meta["cache_last_channel_dim1"])
+        self.cache_last_channel_dim2 = int(meta["cache_last_channel_dim2"])
+        self.cache_last_channel_dim3 = int(meta["cache_last_channel_dim3"])
+
+        self.cache_last_time_dim1 = int(meta["cache_last_time_dim1"])
+        self.cache_last_time_dim2 = int(meta["cache_last_time_dim2"])
+        self.cache_last_time_dim3 = int(meta["cache_last_time_dim3"])
+
+        self.init_cache_state()
+
+    def init_cache_state(self):
+        self.cache_last_channel = torch.zeros(
+            1,
+            self.cache_last_channel_dim1,
+            self.cache_last_channel_dim2,
+            self.cache_last_channel_dim3,
+            dtype=torch.float32,
+        ).numpy()
+
+        self.cache_last_time = torch.zeros(
+            1,
+            self.cache_last_time_dim1,
+            self.cache_last_time_dim2,
+            self.cache_last_time_dim3,
+            dtype=torch.float32,
+        ).numpy()
+
+        self.cache_last_channel_len = torch.ones([1], dtype=torch.int64).numpy()
+
+    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,
+            log_probs_len,
+            cache_last_channel_next,
+            cache_last_time_next,
+            cache_last_channel_len_next,
+        ) = self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+                self.model.get_outputs()[1].name,
+                self.model.get_outputs()[2].name,
+                self.model.get_outputs()[3].name,
+                self.model.get_outputs()[4].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: x.numpy(),
+                self.model.get_inputs()[1].name: x_lens.numpy(),
+                self.model.get_inputs()[2].name: self.cache_last_channel,
+                self.model.get_inputs()[3].name: self.cache_last_time,
+                self.model.get_inputs()[4].name: self.cache_last_channel_len,
+            },
+        )
+        self.cache_last_channel = cache_last_channel_next
+        self.cache_last_time = cache_last_time_next
+        self.cache_last_channel_len = cache_last_channel_len_next
+
+        # [T, vocab_size]
+        return torch.from_numpy(log_probs).squeeze(0)
+
+
+def main():
+    args = get_args()
+    assert Path(args.model).is_file(), args.model
+    assert Path(args.tokens).is_file(), args.tokens
+    assert Path(args.wav).is_file(), args.wav
+
+    print(vars(args))
+
+    model = OnnxModel(args.model)
+
+    id2token = dict()
+    with open(args.tokens, encoding="utf-8") as f:
+        for line in f:
+            t, idx = line.split()
+            id2token[int(idx)] = t
+
+    fbank = create_fbank()
+    audio, sample_rate = sf.read(args.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
+
+    window_size = model.window_size
+    chunk_shift = model.chunk_shift
+
+    blank = len(id2token) - 1
+    prev = -1
+    ans = []
+
+    features = compute_features(audio, fbank)
+    num_chunks = (features.shape[0] - window_size) // chunk_shift + 1
+    for i in range(num_chunks):
+        start = i * chunk_shift
+        end = start + window_size
+        chunk = features[start:end, :]
+
+        log_probs = model(chunk)
+        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]
+    underline = "▁"
+    #  underline = b"\xe2\x96\x81".decode()
+    text = "".join(tokens).replace(underline, " ").strip()
+    print(args.wav)
+    print(text)
+
+
+main()