test exported sense voice models (#1147)

@@ -158,6 +158,7 @@ def main():
     meta_data = {
         "lfr_window_size": lfr_window_size,
         "lfr_window_shift": lfr_window_shift,
+        "normalize_samples": 0,  # input should be in the range [-32768, 32767]
         "neg_mean": neg_mean,
         "inv_stddev": inv_stddev,
         "model_type": "sense_voice_ctc",

@@ -35,3 +35,28 @@ echo "pwd: $PWD"
 ./show-info.py
 
 ls -lh
+
+# Download test wavs
+curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/resolve/main/test_wavs/en.wav
+curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/resolve/main/test_wavs/zh.wav
+curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/resolve/main/test_wavs/ja.wav
+curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/resolve/main/test_wavs/ko.wav
+curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17/resolve/main/test_wavs/yue.wav
+
+for m in model.onnx model.int8.onnx; do
+  for w in en zh ja ko yue; do
+    echo "----------test $m $w.wav----------"
+
+    echo "without inverse text normalization, lang auto"
+    ./test.py --model $m --tokens ./tokens.txt --wav $w.wav --use-itn 0
+
+    echo "with inverse text normalization, lang auto"
+    ./test.py --model $m --tokens ./tokens.txt --wav $w.wav --use-itn 1
+
+    echo "without inverse text normalization, lang $w"
+    ./test.py --model $m --tokens ./tokens.txt --wav $w.wav --use-itn 0 --lang $w
+
+    echo "with inverse text normalization, lang $w"
+    ./test.py --model $m --tokens ./tokens.txt --wav $w.wav --use-itn 1 --lang $w
+  done
+done

+#!/usr/bin/env python3
+# Copyright      2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+import argparse
+from typing import Tuple
+
+import kaldi_native_fbank as knf
+import numpy as np
+import onnxruntime
+import onnxruntime as ort
+import soundfile as sf
+import torch
+
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    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(
+        "--wave",
+        type=str,
+        required=True,
+        help="The input wave to be recognized",
+    )
+
+    parser.add_argument(
+        "--language",
+        type=str,
+        default="auto",
+        help="the language of the input wav file. Supported values: zh, en, ja, ko, yue, auto",
+    )
+
+    parser.add_argument(
+        "--use-itn",
+        type=int,
+        default=0,
+        help="1 to use inverse text normalization. 0 to not use inverse text normalization",
+    )
+
+    return parser.parse_args()
+
+
+class OnnxModel:
+    def __init__(self, filename):
+        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
+
+        self.window_size = int(meta["lfr_window_size"])  # lfr_m
+        self.window_shift = int(meta["lfr_window_shift"])  # lfr_n
+
+        lang_zh = int(meta["lang_zh"])
+        lang_en = int(meta["lang_en"])
+        lang_ja = int(meta["lang_ja"])
+        lang_ko = int(meta["lang_ko"])
+        lang_auto = int(meta["lang_auto"])
+
+        self.lang_id = {
+            "zh": lang_zh,
+            "en": lang_en,
+            "ja": lang_ja,
+            "ko": lang_ko,
+            "auto": lang_auto,
+        }
+        self.with_itn = int(meta["with_itn"])
+        self.without_itn = int(meta["without_itn"])
+
+        neg_mean = meta["neg_mean"].split(",")
+        neg_mean = list(map(lambda x: float(x), neg_mean))
+
+        inv_stddev = meta["inv_stddev"].split(",")
+        inv_stddev = list(map(lambda x: float(x), inv_stddev))
+
+        self.neg_mean = np.array(neg_mean, dtype=np.float32)
+        self.inv_stddev = np.array(inv_stddev, dtype=np.float32)
+
+    def __call__(self, x, x_length, language, text_norm):
+        logits = self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: x.numpy(),
+                self.model.get_inputs()[1].name: x_length.numpy(),
+                self.model.get_inputs()[2].name: language.numpy(),
+                self.model.get_inputs()[3].name: text_norm.numpy(),
+            },
+        )[0]
+
+        return torch.from_numpy(logits)
+
+
+def load_audio(filename: str) -> Tuple[np.ndarray, int]:
+    data, sample_rate = sf.read(
+        filename,
+        always_2d=True,
+        dtype="float32",
+    )
+    data = data[:, 0]  # use only the first channel
+    samples = np.ascontiguousarray(data)
+    return samples, sample_rate
+
+
+def load_tokens(filename):
+    ans = dict()
+    i = 0
+    with open(filename, encoding="utf-8") as f:
+        for line in f:
+            ans[i] = line.strip().split()[0]
+            i += 1
+    return ans
+
+
+def compute_feat(
+    samples,
+    sample_rate,
+    neg_mean: np.ndarray,
+    inv_stddev: np.ndarray,
+    window_size: int = 7,  # lfr_m
+    window_shift: int = 6,  # lfr_n
+):
+    opts = knf.FbankOptions()
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.window_type = "hamming"
+    opts.frame_opts.samp_freq = sample_rate
+    opts.mel_opts.num_bins = 80
+
+    online_fbank = knf.OnlineFbank(opts)
+    online_fbank.accept_waveform(sample_rate, (samples * 32768).tolist())
+    online_fbank.input_finished()
+
+    features = np.stack(
+        [online_fbank.get_frame(i) for i in range(online_fbank.num_frames_ready)]
+    )
+    assert features.data.contiguous is True
+    assert features.dtype == np.float32, features.dtype
+
+    T = (features.shape[0] - window_size) // window_shift + 1
+    features = np.lib.stride_tricks.as_strided(
+        features,
+        shape=(T, features.shape[1] * window_size),
+        strides=((window_shift * features.shape[1]) * 4, 4),
+    )
+
+    features = (features + neg_mean) * inv_stddev
+
+    return features
+
+
+def main():
+    args = get_args()
+    print(vars(args))
+    samples, sample_rate = load_audio(args.wave)
+    if sample_rate != 16000:
+        import librosa
+
+        samples = librosa.resample(samples, orig_sr=sample_rate, target_sr=16000)
+        sample_rate = 16000
+
+    model = OnnxModel(filename=args.model)
+
+    features = compute_feat(
+        samples=samples,
+        sample_rate=sample_rate,
+        neg_mean=model.neg_mean,
+        inv_stddev=model.inv_stddev,
+        window_size=model.window_size,
+        window_shift=model.window_shift,
+    )
+
+    features = torch.from_numpy(features).unsqueeze(0)
+    features_length = torch.tensor([features.size(1)], dtype=torch.int32)
+
+    language = model.lang_id["auto"]
+    if args.language in model.lang_id:
+        language = model.lang_id[args.language]
+    else:
+        print(f"Invalid language: '{args.language}'")
+        print("Use auto")
+
+    if args.use_itn:
+        text_norm = model.with_itn
+    else:
+        text_norm = model.without_itn
+
+    language = torch.tensor([language], dtype=torch.int32)
+    text_norm = torch.tensor([text_norm], dtype=torch.int32)
+
+    logits = model(
+        x=features,
+        x_length=features_length,
+        language=language,
+        text_norm=text_norm,
+    )
+
+    idx = logits.squeeze(0).argmax(dim=-1)
+    # idx is of shape (T,)
+    idx = torch.unique_consecutive(idx)
+
+    blank_id = 0
+    idx = idx[idx != blank_id].tolist()
+
+    tokens = load_tokens(args.tokens)
+    text = "".join([tokens[i] for i in idx])
+
+    text = text.replace("▁", " ")
+    print(text)
+
+
+if __name__ == "__main__":
+    main()