Fix conflict

add-model-metadata.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+
+# Copyright (c)  2023  Xiaomi Corporation
+# Author: Fangjun Kuang
+
+from typing import Dict
+
+import numpy as np
+import onnx
+
+
+def load_cmvn():
+    neg_mean = None
+    inv_stddev = None
+
+    with open("am.mvn") as f:
+        for line in f:
+            if not line.startswith("<LearnRateCoef>"):
+                continue
+            t = line.split()[3:-1]
+
+            if neg_mean is None:
+                neg_mean = ",".join(t)
+            else:
+                inv_stddev = ",".join(t)
+
+    return neg_mean, inv_stddev
+
+
+def load_lfr_params():
+    with open("config.yaml") as f:
+        for line in f:
+            if "lfr_m" in line:
+                lfr_m = int(line.split()[-1])
+            elif "lfr_n" in line:
+                lfr_n = int(line.split()[-1])
+                break
+    lfr_window_size = lfr_m
+    lfr_window_shift = lfr_n
+    return lfr_window_size, lfr_window_shift
+
+
+def get_vocab_size():
+    with open("tokens.txt") as f:
+        return len(f.readlines())
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    model = onnx.load(filename)
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = value
+
+    onnx.save(model, filename)
+    print(f"Updated {filename}")
+
+
+def main():
+    lfr_window_size, lfr_window_shift = load_lfr_params()
+    neg_mean, inv_stddev = load_cmvn()
+    vocab_size = get_vocab_size()
+
+    meta_data = {
+        "lfr_window_size": str(lfr_window_size),
+        "lfr_window_shift": str(lfr_window_shift),
+        "neg_mean": neg_mean,
+        "inv_stddev": inv_stddev,
+        "model_type": "paraformer",
+        "version": "1",
+        "model_author": "damo",
+        "vocab_size": str(vocab_size),
+        "comment": "speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+    }
+    add_meta_data("model.onnx", meta_data)
+
+
+if __name__ == "__main__":
+    main()

am.mvn

@@ -0,0 +1,8 @@
+<Nnet> 
+<Splice> 560 560
+[ 0 ]
+<AddShift> 560 560 
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+<Rescale> 560 560
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+</Nnet> 

convert-tokens.py

@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+import sys
+from typing import Dict
+
+
+def load_tokens():
+    ans = dict()
+    i = 0
+    with open("tokens.txt", encoding="utf-8") as f:
+        for line in f:
+            if len(line.strip().split()) == 2:
+                sys.exit("Already converted!\nExiting")
+
+            ans[i] = line.strip()
+            i += 1
+    return ans
+
+
+def write_tokens(tokens: Dict[int, str]):
+    with open("new_tokens.txt", "w", encoding="utf-8") as f:
+        for idx, s in tokens.items():
+            f.write(f"{s} {idx}\n")
+
+
+def main():
+    tokens = load_tokens()
+    write_tokens(tokens)
+
+
+if __name__ == "__main__":
+    main()

model.int8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a63ecbd790d027e8554995d7f5d66b7e1629d360a2d1be947263de973c8b913
+size 241595433

model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d6e89731feec68c775f8f756acd960e72ef17c8c87feec9c7b112cbeb0bcff75
+size 868256686

quantize-model.py

@@ -0,0 +1,23 @@
+#!/usr/bin/env python3
+
+import onnx
+from onnxruntime.quantization import QuantType, quantize_dynamic
+
+
+def main():
+    onnx_model = onnx.load("model.onnx")
+    nodes = [n.name for n in onnx_model.graph.node]
+    nodes_to_exclude = [m for m in nodes if "output" in m]
+    print(nodes_to_exclude)
+    quantize_dynamic(
+        model_input="model.onnx",
+        model_output="model.int8.onnx",
+        op_types_to_quantize=["MatMul"],
+        per_channel=True,
+        weight_type=QuantType.QUInt8,
+        nodes_to_exclude=nodes_to_exclude,
+    )
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,3 @@
+kaldi-native-fbank
+librosa
+onnxruntime

test-paraformer-onnx.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python3
+
+# Copyright (c)  2023  Xiaomi Corporation
+# Author: Fangjun Kuang
+
+import kaldi_native_fbank as knf
+import librosa
+import numpy as np
+import onnxruntime
+
+
+def load_cmvn():
+    neg_mean = None
+    inv_std = None
+
+    with open("am.mvn") as f:
+        for line in f:
+            if not line.startswith("<LearnRateCoef>"):
+                continue
+            t = line.split()[3:-1]
+            t = list(map(lambda x: float(x), t))
+
+            if neg_mean is None:
+                neg_mean = np.array(t, dtype=np.float32)
+            else:
+                inv_std = np.array(t, dtype=np.float32)
+
+    return neg_mean, inv_std
+
+
+def compute_feat():
+    sample_rate = 16000
+    samples, _ = librosa.load("jfk.wav", sr=sample_rate)
+    opts = knf.FbankOptions()
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    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
+
+    window_size = 7  # lfr_m
+    window_shift = 6  # lfr_n
+
+    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),
+    )
+    neg_mean, inv_std = load_cmvn()
+    features = (features + neg_mean) * inv_std
+    return features
+
+
+# tokens.txt in paraformer has only one column
+# while it has two columns ins sherpa-onnx.
+# This function can handle tokens.txt from both paraformer and sherpa-onnx
+def load_tokens():
+    ans = dict()
+    i = 0
+    with open("tokens.txt", encoding="utf-8") as f:
+        for line in f:
+            ans[i] = line.strip().split()[0]
+            i += 1
+    return ans
+
+
+def main():
+    features = compute_feat()
+    features = np.expand_dims(features, axis=0)
+    features_length = np.array([features.shape[1]], dtype=np.int32)
+
+    session_opts = onnxruntime.SessionOptions()
+    session_opts.log_severity_level = 3  # error level
+    sess = onnxruntime.InferenceSession("model.onnx", session_opts)
+
+    inputs = {
+        "speech": features,
+        "speech_lengths": features_length,
+    }
+    output_names = ["logits"]
+
+    try:
+        outputs = sess.run(output_names, input_feed=inputs)
+    except ONNXRuntimeError:
+        print("Input wav is silence or noise")
+        return
+
+    log_probs = outputs[0].squeeze(0)
+    y = log_probs.argmax(axis=-1)
+
+    tokens = load_tokens()
+    text = "".join([tokens[i] for i in y if i not in (0, 2)])
+    print(text)
+
+
+if __name__ == "__main__":
+    main()