Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.

funasr/datasets/llm_datasets/datasets.py

@@ -0,0 +1,530 @@
+import torch
+import copy
+
+from funasr.register import tables
+from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
+
+
+@tables.register("dataset_classes", "AudioLLMNARDataset")
+class AudioLLMNARDataset(torch.utils.data.Dataset):
+    """
+    AudioLLMDataset
+    """
+
+    def __init__(
+        self,
+        path,
+        index_ds: str = None,
+        frontend=None,
+        tokenizer=None,
+        int_pad_value: int = -1,
+        float_pad_value: float = 0.0,
+        **kwargs
+    ):
+        """Initialize AudioLLMNARDataset.
+        
+            Args:
+                path: TODO.
+                index_ds: TODO.
+                frontend: Audio frontend for feature extraction.
+                tokenizer: Tokenizer instance for text encoding/decoding.
+                int_pad_value: TODO.
+                float_pad_value: TODO.
+                **kwargs: Additional keyword arguments.
+            """
+        super().__init__()
+        index_ds_class = tables.index_ds_classes.get(index_ds)
+        self.index_ds = index_ds_class(path, **kwargs)
+        preprocessor_speech = kwargs.get("preprocessor_speech", None)
+        if preprocessor_speech:
+            preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
+            preprocessor_speech = preprocessor_speech_class(
+                **kwargs.get("preprocessor_speech_conf", {})
+            )
+        self.preprocessor_speech = preprocessor_speech
+        preprocessor_text = kwargs.get("preprocessor_text", None)
+        if preprocessor_text:
+            preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
+            preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf", {}))
+        self.preprocessor_text = preprocessor_text
+
+        self.frontend = frontend
+        self.fs = 16000 if frontend is None else frontend.fs
+        self.data_type = "sound"
+        self.tokenizer = tokenizer
+
+        self.float_pad_value = float_pad_value
+        self.prompt = kwargs.get("prompt", "Please copy the following text.")
+        self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(
+            self.prompt
+        )  # "USER: \nINSTRUCTION: {}\nINPUT: {}\nASSISTANT: "
+        self.prompt_af = ""
+        self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100)
+        self.int_pad_value = self.IGNORE_INDEX
+
+    def get_source_len(self, index):
+        """Get source len.
+        
+            Args:
+                index: TODO.
+            """
+        item = self.index_ds[index]
+        return self.index_ds.get_source_len(item)
+
+    def get_target_len(self, index):
+        """Get target len.
+        
+            Args:
+                index: TODO.
+            """
+        item = self.index_ds[index]
+        return self.index_ds.get_target_len(item)
+
+    def __len__(self):
+        """Internal: len  ."""
+        return len(self.index_ds)
+
+    def __getitem__(self, index):
+        """Internal: getitem  .
+        
+            Args:
+                index: TODO.
+            """
+        item = self.index_ds[index]
+        source = item["source"]
+        data_src = load_audio_text_image_video(source, fs=self.fs)
+        if self.preprocessor_speech:
+            data_src = self.preprocessor_speech(data_src, fs=self.fs)
+        speech, speech_lengths = extract_fbank(
+            data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
+        )  # speech: [b, T, d]
+        speech = speech.squeeze(0)
+
+        target = item["target"]
+        if self.preprocessor_text:
+            target = self.preprocessor_text(target)
+
+        prompt_ids_pre = self.tokenizer.encode(self.prompt_pre)  # [bos,prompt]
+        prompt_ids_length = len(prompt_ids_pre)
+
+        # bos prompt audio bos target
+        # prompt_input = "{}{}".format(self.prompt_pre, target)
+        # prompt_input_ids = self.tokenizer.encode(prompt_input) #[bos, prompt, input]
+        # audio_length = len(prompt_input_ids) - prompt_ids_length
+        target_ids = self.tokenizer.encode(target)
+        if target_ids[0] == self.tokenizer.bos_token_id:
+            target_ids = target_ids[1:]
+        target_ids_length = len(target_ids)
+        audio_length = target_ids_length
+        input_ids = (
+            prompt_ids_pre + target_ids + [self.tokenizer.pad_token_id] + target_ids
+        )  # [bos, prompt, input, pad, target]
+        input_ids = torch.tensor(
+            copy.deepcopy(input_ids), dtype=torch.int64
+        )  # [bos, prompt, input, pad, target]
+        input_ids[prompt_ids_length : prompt_ids_length + audio_length] = (
+            -1
+        )  # [bos, prompt,-1, pad, target] # it is no need, only for check
+        attention_mask = input_ids.ge(-1)  # [true, true, true, true, true], length mask
+
+        # bos prompt audio target eos
+        # prompt_answer = "{}{}".format(self.prompt_pre, target)
+        # prompt_answer_ids = self.tokenizer.encode(prompt_answer) #[bos, prompt, input]
+        # answer_length = len(prompt_answer_ids) - prompt_ids_length
+        target_ids = self.tokenizer.encode(target)
+        if target_ids[0] == self.tokenizer.bos_token_id:
+            target_ids = target_ids[1:]
+        # target_ids_length = len(target_ids)
+        labels_ids = (
+            prompt_ids_pre + target_ids + target_ids + [self.tokenizer.eos_token_id]
+        )  # [bos, prompt, input, target, eos]
+        labels_ids = torch.tensor(
+            copy.deepcopy(labels_ids), dtype=torch.int64
+        )  # [bos, prompt, input, target, eos]
+        labels_ids[:prompt_ids_length] = -1  # [-1, -1, input, target, eos]
+        label_mask = labels_ids.ge(0)  # [false, false, true, true, true], length mask
+        labels_ids[~label_mask] = self.IGNORE_INDEX  # [-1, -1, input, target, eos]
+
+        audio_mask = (
+            [0] * prompt_ids_length + [1] * audio_length + [0] * target_ids_length + [0]
+        )  # [0, 0, 1, 0, 0]
+        audio_mask = torch.tensor(audio_mask, dtype=torch.float32)
+
+        ids = target_ids  # self.tokenizer.encode(target) # token ids is different from labels_ids
+        text = torch.tensor(ids, dtype=torch.int64)
+        text_lengths = torch.tensor([len(ids)], dtype=torch.int32)
+
+        prompt_bos_length = torch.tensor([len(prompt_ids_pre)], dtype=torch.int32)
+
+        return {
+            "speech": speech,
+            "speech_lengths": speech_lengths,
+            "text": text,
+            "text_lengths": text_lengths,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels_ids": labels_ids,
+            "label_mask": label_mask,
+            "audio_mask": audio_mask,
+            "prompt_bos_length": prompt_bos_length,
+        }
+
+    def collator(self, samples: list = None):
+        """Collator.
+        
+            Args:
+                samples: TODO.
+            """
+        outputs = {}
+        for sample in samples:
+            for key in sample.keys():
+                if key not in outputs:
+                    outputs[key] = []
+                outputs[key].append(sample[key])
+
+        for key, data_list in outputs.items():
+            if isinstance(data_list[0], torch.Tensor):
+                if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
+
+                    pad_value = self.int_pad_value
+                else:
+                    pad_value = self.float_pad_value
+
+                outputs[key] = torch.nn.utils.rnn.pad_sequence(
+                    data_list, batch_first=True, padding_value=pad_value
+                )
+        return outputs
+
+
+@tables.register("dataset_classes", "AudioLLMDataset")
+class AudioLLMDataset(torch.utils.data.Dataset):
+    """
+    AudioLLMDataset
+    """
+
+    def __init__(
+        self,
+        path,
+        index_ds: str = None,
+        frontend=None,
+        tokenizer=None,
+        int_pad_value: int = -1,
+        float_pad_value: float = 0.0,
+        **kwargs
+    ):
+        """Initialize AudioLLMDataset.
+        
+            Args:
+                path: TODO.
+                index_ds: TODO.
+                frontend: Audio frontend for feature extraction.
+                tokenizer: Tokenizer instance for text encoding/decoding.
+                int_pad_value: TODO.
+                float_pad_value: TODO.
+                **kwargs: Additional keyword arguments.
+            """
+        super().__init__()
+        index_ds_class = tables.index_ds_classes.get(index_ds)
+        self.index_ds = index_ds_class(path, **kwargs)
+        preprocessor_speech = kwargs.get("preprocessor_speech", None)
+        if preprocessor_speech:
+            preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
+            preprocessor_speech = preprocessor_speech_class(
+                **kwargs.get("preprocessor_speech_conf", {})
+            )
+        self.preprocessor_speech = preprocessor_speech
+        preprocessor_text = kwargs.get("preprocessor_text", None)
+        if preprocessor_text:
+            preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
+            preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf", {}))
+        self.preprocessor_text = preprocessor_text
+
+        self.frontend = frontend
+        self.fs = 16000 if frontend is None else frontend.fs
+        self.data_type = "sound"
+        self.tokenizer = tokenizer
+
+        self.float_pad_value = float_pad_value
+        self.prompt = kwargs.get("prompt", "Transcribe speech to text.")
+        self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(
+            self.prompt
+        )  # "USER: \nINSTRUCTION: {}\nnINPUT: {}\nASSISTANT: "
+        self.prompt_af = ""
+        self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100)
+        self.int_pad_value = self.IGNORE_INDEX
+
+    def get_source_len(self, index):
+        """Get source len.
+        
+            Args:
+                index: TODO.
+            """
+        item = self.index_ds[index]
+        return self.index_ds.get_source_len(item)
+
+    def get_target_len(self, index):
+        """Get target len.
+        
+            Args:
+                index: TODO.
+            """
+        item = self.index_ds[index]
+        return self.index_ds.get_target_len(item)
+
+    def __len__(self):
+        """Internal: len  ."""
+        return len(self.index_ds)
+
+    def __getitem__(self, index):
+        """Internal: getitem  .
+        
+            Args:
+                index: TODO.
+            """
+        item = self.index_ds[index]
+        # import pdb;
+        # pdb.set_trace()
+        source = item["source"]
+        data_src = load_audio_text_image_video(source, fs=self.fs)
+        if self.preprocessor_speech:
+            data_src = self.preprocessor_speech(data_src, fs=self.fs)
+        speech, speech_lengths = extract_fbank(
+            data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
+        )  # speech: [b, T, d]
+        speech = speech.squeeze(0)
+
+        target = item["target"]
+        if self.preprocessor_text:
+            target = self.preprocessor_text(target)
+
+        prompt_ids_pre = self.tokenizer.encode(self.prompt_pre)  # [bos,prompt]
+        prompt_ids_length = len(prompt_ids_pre)
+
+        prompt_input = "{}{}".format(self.prompt_pre, target)
+        prompt_input_ids = self.tokenizer.encode(prompt_input)
+        audio_length = len(prompt_input_ids) - prompt_ids_length
+        input_ids = prompt_input_ids + [self.tokenizer.pad_token_id]
+        input_ids = torch.tensor(input_ids, dtype=torch.int64)  # [bos, prompt, input, pad]
+        input_ids[prompt_ids_length:] = -1  # [bos, prompt,-1,-1]
+        attention_mask = input_ids.ge(-1)  # [true, true, true, true], length mask
+
+        prompt_answer = "{}{}".format(self.prompt_pre, target)
+        prompt_answer_ids = self.tokenizer.encode(prompt_answer)
+        answer_length = len(prompt_answer_ids) - prompt_ids_length
+        labels_ids = copy.deepcopy(prompt_input_ids) + [self.tokenizer.eos_token_id]
+        labels_ids = torch.tensor(labels_ids, dtype=torch.int64)  # [bos, prompt, input, eos]
+        labels_ids[:prompt_ids_length] = -1  # [-1, -1, input, eos]
+        label_mask = labels_ids.ge(0)  # [False,False,True,True]
+        labels_ids[~label_mask] = self.IGNORE_INDEX  # [-100,-100,input,eos]
+
+        audio_mask = [0] * prompt_ids_length + [1] * audio_length + [0]
+        audio_mask = torch.tensor(audio_mask, dtype=torch.float32)
+
+        ids = self.tokenizer.encode(target)  # token ids is different from labels_ids
+        text = torch.tensor(ids, dtype=torch.int64)
+        text_lengths = torch.tensor([len(ids)], dtype=torch.int32)
+
+        return {
+            "speech": speech,
+            "speech_lengths": speech_lengths,
+            "text": text,
+            "text_lengths": text_lengths,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels_ids": labels_ids,
+            "label_mask": label_mask,
+            "audio_mask": audio_mask,
+        }
+
+    def collator(self, samples: list = None):
+        """Collator.
+        
+            Args:
+                samples: TODO.
+            """
+        outputs = {}
+        for sample in samples:
+            for key in sample.keys():
+                if key not in outputs:
+                    outputs[key] = []
+                outputs[key].append(sample[key])
+
+        for key, data_list in outputs.items():
+            if isinstance(data_list[0], torch.Tensor):
+                if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
+
+                    pad_value = self.int_pad_value
+                else:
+                    pad_value = self.float_pad_value
+
+                outputs[key] = torch.nn.utils.rnn.pad_sequence(
+                    data_list, batch_first=True, padding_value=pad_value
+                )
+        return outputs
+
+
+@tables.register("dataset_classes", "AudioLLMARDataset")
+class AudioLLMARDataset(torch.utils.data.Dataset):
+    """
+    AudioLLMDataset
+    """
+
+    def __init__(
+        self,
+        path,
+        index_ds: str = None,
+        frontend=None,
+        tokenizer=None,
+        int_pad_value: int = -1,
+        float_pad_value: float = 0.0,
+        **kwargs
+    ):
+        """Initialize AudioLLMARDataset.
+        
+            Args:
+                path: TODO.
+                index_ds: TODO.
+                frontend: Audio frontend for feature extraction.
+                tokenizer: Tokenizer instance for text encoding/decoding.
+                int_pad_value: TODO.
+                float_pad_value: TODO.
+                **kwargs: Additional keyword arguments.
+            """
+        super().__init__()
+        index_ds_class = tables.index_ds_classes.get(index_ds)
+        self.index_ds = index_ds_class(path, **kwargs)
+        preprocessor_speech = kwargs.get("preprocessor_speech", None)
+        if preprocessor_speech:
+            preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
+            preprocessor_speech = preprocessor_speech_class(
+                **kwargs.get("preprocessor_speech_conf", {})
+            )
+        self.preprocessor_speech = preprocessor_speech
+        preprocessor_text = kwargs.get("preprocessor_text", None)
+        if preprocessor_text:
+            preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
+            preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf", {}))
+        self.preprocessor_text = preprocessor_text
+
+        self.frontend = frontend
+        self.fs = 16000 if frontend is None else frontend.fs
+        self.data_type = "sound"
+        self.tokenizer = tokenizer
+
+        self.float_pad_value = float_pad_value
+        self.prompt = kwargs.get("prompt", "Transcribe speech to text.")
+        self.prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(
+            self.prompt
+        )  # "USER: \nINSTRUCTION: {}\nnINPUT: {}\nASSISTANT: "
+        self.prompt_af = ""
+        self.IGNORE_INDEX = kwargs.get("IGNORE_INDEX", -100)
+        self.int_pad_value = self.IGNORE_INDEX
+
+    def get_source_len(self, index):
+        """Get source len.
+        
+            Args:
+                index: TODO.
+            """
+        item = self.index_ds[index]
+        return self.index_ds.get_source_len(item)
+
+    def get_target_len(self, index):
+        """Get target len.
+        
+            Args:
+                index: TODO.
+            """
+        item = self.index_ds[index]
+        return self.index_ds.get_target_len(item)
+
+    def __len__(self):
+        """Internal: len  ."""
+        return len(self.index_ds)
+
+    def __getitem__(self, index):
+        """Internal: getitem  .
+        
+            Args:
+                index: TODO.
+            """
+        item = self.index_ds[index]
+        # import pdb;
+        # pdb.set_trace()
+        source = item["source"]
+        data_src = load_audio_text_image_video(source, fs=self.fs)
+        if self.preprocessor_speech:
+            data_src = self.preprocessor_speech(data_src, fs=self.fs)
+        speech, speech_lengths = extract_fbank(
+            data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
+        )  # speech: [b, T, d]
+        speech = speech.squeeze(0)
+
+        target = item["target"]
+        if self.preprocessor_text:
+            target = self.preprocessor_text(target)
+
+        prompt_ids_pre = self.tokenizer.encode(self.prompt_pre)  # [bos,prompt]
+        prompt_ids_length = len(prompt_ids_pre)
+
+        prompt_input = "{}{}".format(self.prompt_pre, target)
+        prompt_input_ids = self.tokenizer.encode(prompt_input)
+        audio_length = len(prompt_input_ids) - prompt_ids_length
+        input_ids = prompt_input_ids + [self.tokenizer.pad_token_id]
+        input_ids = torch.tensor(input_ids, dtype=torch.int64)  # [bos, prompt, input, pad]
+        input_ids[prompt_ids_length:] = -1  # [bos, prompt,-1,-1]
+        attention_mask = input_ids.ge(-1)  # [true, true, true, true], length mask
+
+        prompt_answer = "{}{}".format(self.prompt_pre, target)
+        prompt_answer_ids = self.tokenizer.encode(prompt_answer)
+        answer_length = len(prompt_answer_ids) - prompt_ids_length
+        labels_ids = copy.deepcopy(prompt_input_ids) + [self.tokenizer.eos_token_id]
+        labels_ids = torch.tensor(labels_ids, dtype=torch.int64)  # [bos, prompt, input, eos]
+        labels_ids[:prompt_ids_length] = -1  # [-1, -1, input, eos]
+        label_mask = labels_ids.ge(0)  # [False,False,True,True]
+        labels_ids[~label_mask] = self.IGNORE_INDEX  # [-100,-100,input,eos]
+
+        audio_mask = [0] * prompt_ids_length + [1] * audio_length + [0]
+        audio_mask = torch.tensor(audio_mask, dtype=torch.float32)
+
+        ids = self.tokenizer.encode(target)  # token ids is different from labels_ids
+        text = torch.tensor(ids, dtype=torch.int64)
+        text_lengths = torch.tensor([len(ids)], dtype=torch.int32)
+
+        return {
+            "speech": speech,
+            "speech_lengths": speech_lengths,
+            "text": text,
+            "text_lengths": text_lengths,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "labels_ids": labels_ids,
+            "label_mask": label_mask,
+            "audio_mask": audio_mask,
+        }
+
+    def collator(self, samples: list = None):
+        """Collator.
+        
+            Args:
+                samples: TODO.
+            """
+        outputs = {}
+        for sample in samples:
+            for key in sample.keys():
+                if key not in outputs:
+                    outputs[key] = []
+                outputs[key].append(sample[key])
+
+        for key, data_list in outputs.items():
+            if isinstance(data_list[0], torch.Tensor):
+                if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
+
+                    pad_value = self.int_pad_value
+                else:
+                    pad_value = self.float_pad_value
+
+                outputs[key] = torch.nn.utils.rnn.pad_sequence(
+                    data_list, batch_first=True, padding_value=pad_value
+                )
+        return outputs

funasr/datasets/llm_datasets/preprocessor.py

@@ -0,0 +1,45 @@
+import os
+import json
+import torch
+import logging
+import concurrent.futures
+import librosa
+import torch.distributed as dist
+from typing import Collection
+import torch
+import torchaudio
+from torch import nn
+import random
+import re
+import string
+from funasr.tokenizer.cleaner import TextCleaner
+from funasr.register import tables
+
+
+@tables.register("preprocessor_classes", "TextPreprocessRemovePunctuation")
+class TextPreprocessRemovePunctuation(nn.Module):
+    def __init__(self, **kwargs):
+        """Initialize TextPreprocessRemovePunctuation.
+        
+            Args:
+                **kwargs: Additional keyword arguments.
+            """
+        super().__init__()
+
+    def forward(self, text, **kwargs):
+        # 定义英文标点符号
+        """Forward pass for training.
+        
+            Args:
+                text: Text tensor or string input.
+                **kwargs: Additional keyword arguments.
+            """
+        en_punct = string.punctuation
+        # 定义中文标点符号（部分常用的）
+        cn_punct = "。？！，、；：“”‘’（）《》【】…—～·"
+        # 合并英文和中文标点符号
+        all_punct = en_punct + cn_punct
+        # 创建正则表达式模式，匹配任何在all_punct中的字符
+        punct_pattern = re.compile("[{}]".format(re.escape(all_punct)))
+        # 使用正则表达式的sub方法替换掉这些字符
+        return punct_pattern.sub("", text)