Merge pull request #1081 from gongel/st_cli

[ST] Add st cli

paddlespeech/cli/__init__.py

@@ -15,3 +15,4 @@ from .asr import ASRExecutor
 from .base_commands import BaseCommand
 from .base_commands import HelpCommand
 from .cls import CLSExecutor
+from .st import STExecutor

paddlespeech/cli/st/__init__.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from .infer import STExecutor

paddlespeech/cli/st/infer.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import os
+import subprocess
+from typing import List
+from typing import Optional
+from typing import Union
+
+import kaldi_io
+import numpy as np
+import paddle
+import soundfile
+from kaldiio import WriteHelper
+from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
+from paddlespeech.s2t.utils.dynamic_import import dynamic_import
+from paddlespeech.s2t.utils.utility import UpdateConfig
+from yacs.config import CfgNode
+
+from ..executor import BaseExecutor
+from ..utils import cli_register
+from ..utils import download_and_decompress
+from ..utils import logger
+from ..utils import MODEL_HOME
+
+__all__ = ["STExecutor"]
+
+pretrained_models = {
+    "fat_st_ted_en-zh": {
+        "url":
+        "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_ted-en-zh.tar.gz",
+        "md5":
+        "fa0a7425b91b4f8d259c70b2aca5ae67",
+        "cfg_path":
+        "conf/transformer_mtl_noam.yaml",
+        "ckpt_path":
+        "exp/transformer_mtl_noam/checkpoints/fat_st_ted-en-zh.pdparams",
+    }
+}
+
+model_alias = {"fat_st_ted": "paddlespeech.s2t.models.u2_st:U2STModel"}
+
+kaldi_bins = {
+    "url":
+    "https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/kaldi_bins.tar.gz",
+    "md5":
+    "c0682303b3f3393dbf6ed4c4e35a53eb",
+}
+
+
+@cli_register(
+    name="paddlespeech.st", description="Speech translation infer command.")
+class STExecutor(BaseExecutor):
+    def __init__(self):
+        super(STExecutor, self).__init__()
+
+        self.parser = argparse.ArgumentParser(
+            prog="paddlespeech.st", add_help=True)
+        self.parser.add_argument(
+            "--input", type=str, required=True, help="Audio file to translate.")
+        self.parser.add_argument(
+            "--model_type",
+            type=str,
+            default="fat_st_ted",
+            help="Choose model type of st task.")
+        self.parser.add_argument(
+            "--src_lang",
+            type=str,
+            default="en",
+            help="Choose model source language.")
+        self.parser.add_argument(
+            "--tgt_lang",
+            type=str,
+            default="zh",
+            help="Choose model target language.")
+        self.parser.add_argument(
+            "--sample_rate",
+            type=int,
+            default=16000,
+            choices=[16000],
+            help='Choose the audio sample rate of the model. 8000 or 16000')
+        self.parser.add_argument(
+            "--cfg_path",
+            type=str,
+            default=None,
+            help="Config of st task. Use deault config when it is None.")
+        self.parser.add_argument(
+            "--ckpt_path",
+            type=str,
+            default=None,
+            help="Checkpoint file of model.")
+        self.parser.add_argument(
+            "--device",
+            type=str,
+            default=paddle.get_device(),
+            help="Choose device to execute model inference.")
+
+    def _get_pretrained_path(self, tag: str) -> os.PathLike:
+        """
+            Download and returns pretrained resources path of current task.
+        """
+        assert tag in pretrained_models, "Can not find pretrained resources of {}.".format(
+            tag)
+
+        res_path = os.path.join(MODEL_HOME, tag)
+        decompressed_path = download_and_decompress(pretrained_models[tag],
+                                                    res_path)
+        decompressed_path = os.path.abspath(decompressed_path)
+        logger.info(
+            "Use pretrained model stored in: {}".format(decompressed_path))
+
+        return decompressed_path
+
+    def _set_kaldi_bins(self) -> os.PathLike:
+        """
+            Download and returns kaldi_bins resources path of current task.
+        """
+        decompressed_path = download_and_decompress(kaldi_bins, MODEL_HOME)
+        decompressed_path = os.path.abspath(decompressed_path)
+        logger.info("Kaldi_bins stored in: {}".format(decompressed_path))
+        if "LD_LIBRARY_PATH" in os.environ:
+            os.environ["LD_LIBRARY_PATH"] += f":{decompressed_path}"
+        else:
+            os.environ["LD_LIBRARY_PATH"] = f"{decompressed_path}"
+        os.environ["PATH"] += f":{decompressed_path}"
+        return decompressed_path
+
+    def _init_from_path(self,
+                        model_type: str="fat_st_ted",
+                        src_lang: str="en",
+                        tgt_lang: str="zh",
+                        cfg_path: Optional[os.PathLike]=None,
+                        ckpt_path: Optional[os.PathLike]=None):
+        """
+            Init model and other resources from a specific path.
+        """
+        if hasattr(self, 'model'):
+            logger.info('Model had been initialized.')
+            return
+
+        if cfg_path is None or ckpt_path is None:
+            tag = model_type + "_" + src_lang + "-" + tgt_lang
+            res_path = self._get_pretrained_path(tag)
+            self.cfg_path = os.path.join(res_path,
+                                         pretrained_models[tag]["cfg_path"])
+            self.ckpt_path = os.path.join(res_path,
+                                          pretrained_models[tag]["ckpt_path"])
+            logger.info(res_path)
+            logger.info(self.cfg_path)
+            logger.info(self.ckpt_path)
+        else:
+            self.cfg_path = os.path.abspath(cfg_path)
+            self.ckpt_path = os.path.abspath(ckpt_path)
+            res_path = os.path.dirname(
+                os.path.dirname(os.path.abspath(self.cfg_path)))
+
+        #Init body.
+        self.config = CfgNode(new_allowed=True)
+        self.config.merge_from_file(self.cfg_path)
+        self.config.decoding.decoding_method = "fullsentence"
+
+        with UpdateConfig(self.config):
+            self.config.collator.vocab_filepath = os.path.join(
+                res_path, self.config.collator.vocab_filepath)
+            self.config.collator.cmvn_path = os.path.join(
+                res_path, self.config.collator.cmvn_path)
+            self.config.collator.spm_model_prefix = os.path.join(
+                res_path, self.config.collator.spm_model_prefix)
+            self.text_feature = TextFeaturizer(
+                unit_type=self.config.collator.unit_type,
+                vocab_filepath=self.config.collator.vocab_filepath,
+                spm_model_prefix=self.config.collator.spm_model_prefix)
+            self.config.model.input_dim = self.config.collator.feat_dim
+            self.config.model.output_dim = self.text_feature.vocab_size
+
+        model_conf = self.config.model
+        logger.info(model_conf)
+        model_class = dynamic_import(model_type, model_alias)
+        self.model = model_class.from_config(model_conf)
+        self.model.eval()
+
+        # load model
+        params_path = self.ckpt_path
+        model_dict = paddle.load(params_path)
+        self.model.set_state_dict(model_dict)
+
+        # set kaldi bins
+        self._set_kaldi_bins()
+
+    def _check(self, audio_file: str, sample_rate: int):
+        _, audio_sample_rate = soundfile.read(
+            audio_file, dtype="int16", always_2d=True)
+        if audio_sample_rate != sample_rate:
+            raise Exception("invalid sample rate")
+            sys.exit(-1)
+
+    def preprocess(self, wav_file: Union[str, os.PathLike], model_type: str):
+        """
+            Input preprocess and return paddle.Tensor stored in self.input.
+            Input content can be a file(wav).
+        """
+        audio_file = os.path.abspath(wav_file)
+        logger.info("Preprocess audio_file:" + audio_file)
+
+        if model_type == "fat_st_ted":
+            cmvn = self.config.collator.cmvn_path
+            utt_name = "_tmp"
+
+            # Get the object for feature extraction
+            fbank_extract_command = [
+                "compute-fbank-feats", "--num-mel-bins=80", "--verbose=2",
+                "--sample-frequency=16000", "scp:-", "ark:-"
+            ]
+            fbank_extract_process = subprocess.Popen(
+                fbank_extract_command,
+                stdin=subprocess.PIPE,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE)
+            fbank_extract_process.stdin.write(
+                f"{utt_name} {wav_file}".encode("utf8"))
+            fbank_extract_process.stdin.close()
+            fbank_feat = dict(
+                kaldi_io.read_mat_ark(fbank_extract_process.stdout))[utt_name]
+
+            extract_command = ["compute-kaldi-pitch-feats", "scp:-", "ark:-"]
+            pitch_extract_process = subprocess.Popen(
+                extract_command,
+                stdin=subprocess.PIPE,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE)
+            pitch_extract_process.stdin.write(
+                f"{utt_name} {wav_file}".encode("utf8"))
+            process_command = ["process-kaldi-pitch-feats", "ark:", "ark:-"]
+            pitch_process = subprocess.Popen(
+                process_command,
+                stdin=pitch_extract_process.stdout,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE)
+            pitch_extract_process.stdin.close()
+            pitch_feat = dict(
+                kaldi_io.read_mat_ark(pitch_process.stdout))[utt_name]
+            concated_feat = np.concatenate((fbank_feat, pitch_feat), axis=1)
+            raw_feat = f"{utt_name}.raw"
+            with WriteHelper(
+                    f"ark,scp:{raw_feat}.ark,{raw_feat}.scp") as writer:
+                writer(utt_name, concated_feat)
+            cmvn_command = [
+                "apply-cmvn", "--norm-vars=true", cmvn, f"scp:{raw_feat}.scp",
+                "ark:-"
+            ]
+            cmvn_process = subprocess.Popen(
+                cmvn_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+            process_command = [
+                "copy-feats", "--compress=true", "ark:-", "ark:-"
+            ]
+            process = subprocess.Popen(
+                process_command,
+                stdin=cmvn_process.stdout,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE)
+            norm_feat = dict(kaldi_io.read_mat_ark(process.stdout))[utt_name]
+            self._inputs["audio"] = paddle.to_tensor(norm_feat).unsqueeze(0)
+            self._inputs["audio_len"] = paddle.to_tensor(
+                self._inputs["audio"].shape[1], dtype="int64")
+        else:
+            raise ValueError("Wrong model type.")
+
+    @paddle.no_grad()
+    def infer(self, model_type: str):
+        """
+            Model inference and result stored in self.output.
+        """
+        cfg = self.config.decoding
+        audio = self._inputs["audio"]
+        audio_len = self._inputs["audio_len"]
+        if model_type == "fat_st_ted":
+            hyps = self.model.decode(
+                audio,
+                audio_len,
+                text_feature=self.text_feature,
+                decoding_method=cfg.decoding_method,
+                lang_model_path=None,
+                beam_alpha=cfg.alpha,
+                beam_beta=cfg.beta,
+                beam_size=cfg.beam_size,
+                cutoff_prob=cfg.cutoff_prob,
+                cutoff_top_n=cfg.cutoff_top_n,
+                num_processes=cfg.num_proc_bsearch,
+                ctc_weight=cfg.ctc_weight,
+                word_reward=cfg.word_reward,
+                decoding_chunk_size=cfg.decoding_chunk_size,
+                num_decoding_left_chunks=cfg.num_decoding_left_chunks,
+                simulate_streaming=cfg.simulate_streaming)
+            self._outputs["result"] = hyps
+        else:
+            raise ValueError("Wrong model type.")
+
+    def postprocess(self, model_type: str) -> Union[str, os.PathLike]:
+        """
+            Output postprocess and return human-readable results such as texts and audio files.
+        """
+        if model_type == "fat_st_ted":
+            return self._outputs["result"]
+        else:
+            raise ValueError("Wrong model type.")
+
+    def execute(self, argv: List[str]) -> bool:
+        """
+            Command line entry.
+        """
+        parser_args = self.parser.parse_args(argv)
+
+        model_type = parser_args.model_type
+        src_lang = parser_args.src_lang
+        tgt_lang = parser_args.tgt_lang
+        sample_rate = parser_args.sample_rate
+        cfg_path = parser_args.cfg_path
+        ckpt_path = parser_args.ckpt_path
+        audio_file = parser_args.input
+        device = parser_args.device
+
+        try:
+            res = self(model_type, src_lang, tgt_lang, sample_rate, cfg_path,
+                       ckpt_path, audio_file, device)
+            logger.info("ST Result: {}".format(res))
+            return True
+        except Exception as e:
+            print(e)
+            return False
+
+    def __call__(self, model_type, src_lang, tgt_lang, sample_rate, cfg_path,
+                 ckpt_path, audio_file, device):
+        """
+            Python API to call an executor.
+        """
+        audio_file = os.path.abspath(audio_file)
+        self._check(audio_file, sample_rate)
+        paddle.set_device(device)
+        self._init_from_path(model_type, src_lang, tgt_lang, cfg_path,
+                             ckpt_path)
+        self.preprocess(audio_file, model_type)
+        self.infer(model_type)
+        res = self.postprocess(model_type)
+
+        return res

setup.py

@@ -37,6 +37,7 @@ requirements = {
         "jieba",
         "jsonlines",
         "kaldiio",
+        "kaldi_io",
         "librosa",
         "loguru",
         "matplotlib",