# Copyright (c) 2021, NVIDIA CORPORATION. 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 copy import os from dataclasses import dataclass from pathlib import Path from typing import NamedTuple, Optional import librosa import numpy as np import torch from omegaconf import OmegaConf from torch.nn.utils.rnn import pad_sequence from torch.utils.data import DataLoader, Dataset from nemo.collections.asr.data.audio_to_text_lhotse_prompted import PromptedAudioToTextMiniBatch from nemo.collections.asr.models import ASRModel from nemo.collections.asr.parts.mixins.streaming import StreamingEncoder from nemo.collections.asr.parts.preprocessing.features import normalize_batch from nemo.collections.asr.parts.preprocessing.segment import get_samples from nemo.collections.asr.parts.utils import rnnt_utils from nemo.core.classes import IterableDataset from nemo.core.neural_types import LengthsType, MelSpectrogramType, NeuralType # Minimum number of tokens required to assign a LCS merge step, otherwise ignore and # select all i-1 and ith buffer tokens to merge. MIN_MERGE_SUBSEQUENCE_LEN = 1 def print_alignment(alignment): """ Print an alignment matrix of the shape (m + 1, n + 1) Args: alignment: An integer alignment matrix of shape (m + 1, n + 1) """ m = len(alignment) if m > 0: n = len(alignment[0]) for i in range(m): for j in range(n): if j == 0: print(f"{i:4d} |", end=" ") print(f"{alignment[i][j]}", end=" ") print() def write_lcs_alignment_to_pickle(alignment, filepath, extras=None): """ Writes out the LCS alignment to a file, along with any extras provided. Args: alignment: An alignment matrix of shape [m + 1, n + 1] filepath: str filepath extras: Optional dictionary of items to preserve. """ if extras is None: extras = {} extras['alignment'] = alignment torch.save(extras, filepath) def longest_common_subsequence_merge(X, Y, filepath=None): """ Longest Common Subsequence merge algorithm for aligning two consecutive buffers. Base alignment construction algorithm is Longest Common Subsequence (reffered to as LCS hear after) LCS Merge algorithm looks at two chunks i-1 and i, determins the aligned overlap at the end of i-1 and beginning of ith chunk, and then clips the subsegment of the ith chunk. Assumption is that the two chunks are consecutive chunks, and there exists at least small overlap acoustically. It is a sub-word token merge algorithm, operating on the abstract notion of integer ids representing the subword ids. It is independent of text or character encoding. Since the algorithm is merge based, and depends on consecutive buffers, the very first buffer is processes using the "middle tokens" algorithm. It requires a delay of some number of tokens such that: lcs_delay = math.floor(((total_buffer_in_secs - chunk_len_in_sec)) / model_stride_in_secs) Total cost of the model is O(m_{i-1} * n_{i}) where (m, n) represents the number of subword ids of the buffer. Args: X: The subset of the previous chunk i-1, sliced such X = X[-(lcs_delay * max_steps_per_timestep):] Therefore there can be at most lcs_delay * max_steps_per_timestep symbols for X, preserving computation. Y: The entire current chunk i. filepath: Optional filepath to save the LCS alignment matrix for later introspection. Returns: A tuple containing - - i: Start index of alignment along the i-1 chunk. - j: Start index of alignment along the ith chunk. - slice_len: number of tokens to slice off from the ith chunk. The LCS alignment matrix itself (shape m + 1, n + 1) """ # LCSuff is the table with zero # value initially in each cell m = len(X) n = len(Y) LCSuff = [[0 for _ in range(n + 1)] for _ in range(m + 1)] # To store the length of # longest common substring result = 0 result_idx = [0, 0, 0] # Contains (i, j, slice_len) # Following steps to build # LCSuff[m+1][n+1] in bottom up fashion for i in range(m + 1): for j in range(n + 1): if i == 0 or j == 0: LCSuff[i][j] = 0 elif X[i - 1] == Y[j - 1]: LCSuff[i][j] = LCSuff[i - 1][j - 1] + 1 if result <= LCSuff[i][j]: result = LCSuff[i][j] # max(result, LCSuff[i][j]) result_idx = [i, j, result] else: LCSuff[i][j] = 0 # Check if perfect alignment was found or not # Perfect alignment is found if : # Longest common subsequence extends to the final row of of the old buffer # This means that there exists a diagonal LCS backtracking to the beginning of the new buffer i, j = result_idx[0:2] is_complete_merge = i == m # Perfect alignment was found, slice eagerly if is_complete_merge: length = result_idx[-1] # In case the LCS was incomplete - missing a few tokens at the beginning # Perform backtrack to find the origin point of the slice (j) and how many tokens should be sliced while length >= 0 and i > 0 and j > 0: # Alignment exists at the required diagonal if LCSuff[i - 1][j - 1] > 0: length -= 1 i, j = i - 1, j - 1 else: # End of longest alignment i, j, length = i - 1, j - 1, length - 1 break else: # Expand hypothesis to catch partial mismatch # There are 3 steps for partial mismatch in alignment # 1) Backward search for leftmost LCS # 2) Greedy expansion of leftmost LCS to the right # 3) Backtrack final leftmost expanded LCS to find origin point of slice # (1) Backward search for Leftmost LCS # This is required for cases where multiple common subsequences exist # We only need to select the leftmost one - since that corresponds # to the last potential subsequence that matched with the new buffer. # If we just chose the LCS (and not the leftmost LCS), then we can potentially # slice off major sections of text which are repeated between two overlapping buffers. # backward linear search for leftmost j with longest subsequence max_j = 0 max_j_idx = n i_partial = m # Starting index of i for partial merge j_partial = -1 # Index holder of j for partial merge j_skip = 0 # Number of tokens that were skipped along the diagonal slice_count = 0 # Number of tokens that should be sliced # Select leftmost LCS for i_idx in range(m, -1, -1): # start from last timestep of old buffer for j_idx in range(0, n + 1): # start from first token from new buffer # Select the longest LCSuff, while minimizing the index of j (token index for new buffer) if LCSuff[i_idx][j_idx] > max_j and j_idx <= max_j_idx: max_j = LCSuff[i_idx][j_idx] max_j_idx = j_idx # Update the starting indices of the partial merge i_partial = i_idx j_partial = j_idx # EARLY EXIT (if max subsequence length <= MIN merge length) # Important case where there is long silence # The end of one buffer will have many blank tokens, the beginning of new buffer may have many blank tokens # As such, LCS will potentially be from the region of actual tokens. # This can be detected as the max length of the suffix in LCS # If this max length of the leftmost suffix is less than some margin, avoid slicing all together. if max_j <= MIN_MERGE_SUBSEQUENCE_LEN: # If the number of partiial tokens to be deleted are less than the minimum, # dont delete any tokens at all. i = i_partial j = 0 result_idx[-1] = 0 else: # Some valid long partial alignment was found # (2) Expand this alignment along the diagonal *downwards* towards the end of the old buffer # such that i_partial = m + 1. # This is a common case where due to LSTM state or reduced buffer size, the alignment breaks # in the middle but there are common subsequences between old and new buffers towards the end # We can expand the current leftmost LCS in a diagonal manner downwards to include such potential # merge regions. # Expand current partial subsequence with co-located tokens i_temp = i_partial + 1 # diagonal next i j_temp = j_partial + 1 # diagonal next j j_exp = 0 # number of tokens to expand along the diagonal j_skip = 0 # how many diagonals didnt have the token. Incremented by 1 for every row i for i_idx in range(i_temp, m + 1): # walk from i_partial + 1 => m + 1 j_any_skip = 0 # If the diagonal element at this location is not found, set to 1 # j_any_skip expands the search space one place to the right # This allows 1 diagonal misalignment per timestep i (and expands the search for the next timestep) # walk along the diagonal corresponding to i_idx, plus allowing diagonal skips to occur # diagonal elements may not be aligned due to ASR model predicting # incorrect token in between correct tokens for j_idx in range(j_temp, j_temp + j_skip + 1): if j_idx < n + 1: if LCSuff[i_idx][j_idx] == 0: j_any_skip = 1 else: j_exp = 1 + j_skip + j_any_skip # If the diagonal element existed, dont expand the search space, # otherwise expand the search space 1 token to the right j_skip += j_any_skip # Move one step to the right for the next diagonal j corresponding to i j_temp += 1 # reset j_skip, augment j_partial with expansions j_skip = 0 j_partial += j_exp # (3) Given new leftmost j_partial with expansions, backtrack the partial alignments # counting how many diagonal skips occured to compute slice length # as well as starting point of slice. # Partial backward trace to find start of slice while i_partial > 0 and j_partial > 0: if LCSuff[i_partial][j_partial] == 0: # diagonal skip occured, move j to left 1 extra time j_partial -= 1 j_skip += 1 if j_partial > 0: # If there are more steps to be taken to the left, slice off the current j # Then loop for next (i, j) diagonal to the upper left slice_count += 1 i_partial -= 1 j_partial -= 1 # Recompute total slice length as slice count along diagonal # plus the number of diagonal skips i = max(0, i_partial) j = max(0, j_partial) result_idx[-1] = slice_count + j_skip # Set the value of i and j result_idx[0] = i result_idx[1] = j if filepath is not None: extras = { "is_complete_merge": is_complete_merge, "X": X, "Y": Y, "slice_idx": result_idx, } write_lcs_alignment_to_pickle(LCSuff, filepath=filepath, extras=extras) print("Wrote alignemnt to :", filepath) return result_idx, LCSuff def lcs_alignment_merge_buffer(buffer, data, delay, model, max_steps_per_timestep: int = 5, filepath: str = None): """ Merges the new text from the current frame with the previous text contained in the buffer. The alignment is based on a Longest Common Subsequence algorithm, with some additional heuristics leveraging the notion that the chunk size is >= the context window. In case this assumptio is violated, the results of the merge will be incorrect (or at least obtain worse WER overall). """ # If delay timesteps is 0, that means no future context was used. Simply concatenate the buffer with new data. if delay < 1: buffer += data return buffer # If buffer is empty, simply concatenate the buffer and data. if len(buffer) == 0: buffer += data return buffer # Prepare a subset of the buffer that will be LCS Merged with new data search_size = int(delay * max_steps_per_timestep) buffer_slice = buffer[-search_size:] # Perform LCS Merge lcs_idx, lcs_alignment = longest_common_subsequence_merge(buffer_slice, data, filepath=filepath) # Slice off new data # i, j, slice_len = lcs_idx slice_idx = lcs_idx[1] + lcs_idx[-1] # slice = j + slice_len data = data[slice_idx:] # Concat data to buffer buffer += data return buffer def inplace_buffer_merge(buffer, data, timesteps, model): """ Merges the new text from the current frame with the previous text contained in the buffer. The alignment is based on a Longest Common Subsequence algorithm, with some additional heuristics leveraging the notion that the chunk size is >= the context window. In case this assumptio is violated, the results of the merge will be incorrect (or at least obtain worse WER overall). """ # If delay timesteps is 0, that means no future context was used. Simply concatenate the buffer with new data. if timesteps < 1: buffer += data return buffer # If buffer is empty, simply concatenate the buffer and data. if len(buffer) == 0: buffer += data return buffer # Concat data to buffer buffer += data return buffer class StreamingFeatureBufferer: """ Class to append each feature frame to a buffer and return an array of buffers. This class is designed to perform a real-life streaming decoding where only a single chunk is provided at each step of a streaming pipeline. """ def __init__(self, asr_model, chunk_size, buffer_size): ''' Args: asr_model: Reference to the asr model instance for which the feature needs to be created chunk_size (float): Duration of the new chunk of audio buffer_size (float): Size of the total audio in seconds maintained in the buffer ''' self.NORM_CONSTANT = 1e-5 if hasattr(asr_model.preprocessor, 'log') and asr_model.preprocessor.log: self.ZERO_LEVEL_SPEC_DB_VAL = -16.635 # Log-Melspectrogram value for zero signal else: self.ZERO_LEVEL_SPEC_DB_VAL = 0.0 self.asr_model = asr_model self.sr = asr_model.cfg.sample_rate self.model_normalize_type = asr_model.cfg.preprocessor.normalize self.chunk_size = chunk_size timestep_duration = asr_model.cfg.preprocessor.window_stride self.n_chunk_look_back = int(timestep_duration * self.sr) self.n_chunk_samples = int(chunk_size * self.sr) self.buffer_size = buffer_size total_buffer_len = int(buffer_size / timestep_duration) self.n_feat = asr_model.cfg.preprocessor.features self.sample_buffer = torch.zeros(int(self.buffer_size * self.sr)) self.buffer = torch.ones([self.n_feat, total_buffer_len], dtype=torch.float32) * self.ZERO_LEVEL_SPEC_DB_VAL self.feature_chunk_len = int(chunk_size / timestep_duration) self.feature_buffer_len = total_buffer_len self.reset() cfg = copy.deepcopy(asr_model.cfg) OmegaConf.set_struct(cfg.preprocessor, False) cfg.preprocessor.dither = 0.0 cfg.preprocessor.pad_to = 0 cfg.preprocessor.normalize = "None" self.raw_preprocessor = ASRModel.from_config_dict(cfg.preprocessor) self.raw_preprocessor.to(asr_model.device) def reset(self): ''' Reset frame_history and decoder's state ''' self.buffer = torch.ones(self.buffer.shape, dtype=torch.float32) * self.ZERO_LEVEL_SPEC_DB_VAL self.frame_buffers = [] self.sample_buffer = torch.zeros(int(self.buffer_size * self.sr)) self.feature_buffer = ( torch.ones([self.n_feat, self.feature_buffer_len], dtype=torch.float32) * self.ZERO_LEVEL_SPEC_DB_VAL ) def _add_chunk_to_buffer(self, chunk): """ Add time-series audio signal to `sample_buffer` Args: chunk (Tensor): Tensor filled with time-series audio signal """ self.sample_buffer[: -self.n_chunk_samples] = self.sample_buffer[self.n_chunk_samples :].clone() self.sample_buffer[-self.n_chunk_samples :] = chunk.clone() def _update_feature_buffer(self, feat_chunk): """ Add an extracted feature to `feature_buffer` """ self.feature_buffer[:, : -self.feature_chunk_len] = self.feature_buffer[:, self.feature_chunk_len :].clone() self.feature_buffer[:, -self.feature_chunk_len :] = feat_chunk.clone() def get_raw_feature_buffer(self): return self.feature_buffer def get_normalized_feature_buffer(self): normalized_buffer, _, _ = normalize_batch( x=self.feature_buffer.unsqueeze(0), seq_len=torch.tensor([len(self.feature_buffer)]), normalize_type=self.model_normalize_type, ) return normalized_buffer.squeeze(0) def _convert_buffer_to_features(self): """ Extract features from the time-series audio buffer `sample_buffer`. """ # samples for conversion to features. # Add look_back to have context for the first feature samples = self.sample_buffer[: -(self.n_chunk_samples + self.n_chunk_look_back)] device = self.asr_model.device audio_signal = samples.unsqueeze_(0).to(device) audio_signal_len = torch.Tensor([samples.shape[1]]).to(device) features, features_len = self.raw_preprocessor( input_signal=audio_signal, length=audio_signal_len, ) features = features.squeeze() self._update_feature_buffer(features[:, -self.feature_chunk_len :]) def update_feature_buffer(self, chunk): """ Update time-series signal `chunk` to the buffer then generate features out of the signal in the audio buffer. Args: chunk (Tensor): Tensor filled with time-series audio signal """ if len(chunk) > self.n_chunk_samples: raise ValueError(f"chunk should be of length {self.n_chunk_samples} or less") if len(chunk) < self.n_chunk_samples: temp_chunk = torch.zeros(self.n_chunk_samples, dtype=torch.float32) temp_chunk[: chunk.shape[0]] = chunk chunk = temp_chunk self._add_chunk_to_buffer(chunk) self._convert_buffer_to_features() class AudioFeatureIterator(IterableDataset): def __init__(self, samples, frame_len, preprocessor, device, pad_to_frame_len=True): self._samples = samples self._frame_len = frame_len self._start = 0 self.output = True self.count = 0 self.pad_to_frame_len = pad_to_frame_len timestep_duration = preprocessor._cfg['window_stride'] self._feature_frame_len = frame_len / timestep_duration audio_signal = torch.from_numpy(self._samples).unsqueeze_(0).to(device) audio_signal_len = torch.Tensor([self._samples.shape[0]]).to(device) self._features, self._features_len = preprocessor( input_signal=audio_signal, length=audio_signal_len, ) self._features = self._features.squeeze() def __iter__(self): return self def __next__(self): if not self.output: raise StopIteration last = int(self._start + self._feature_frame_len) if last <= self._features_len[0]: frame = self._features[:, self._start : last].cpu() self._start = last else: if not self.pad_to_frame_len: frame = self._features[:, self._start : self._features_len[0]].cpu() else: frame = np.zeros([self._features.shape[0], int(self._feature_frame_len)], dtype='float32') segment = self._features[:, self._start : self._features_len[0]].cpu() frame[:, : segment.shape[1]] = segment self.output = False self.count += 1 return frame def speech_collate_fn(batch): """collate batch of audio sig, audio len, tokens, tokens len Args: batch (Optional[FloatTensor], Optional[LongTensor], LongTensor, LongTensor): A tuple of tuples of signal, signal lengths, encoded tokens, and encoded tokens length. This collate func assumes the signals are 1d torch tensors (i.e. mono audio). """ _, audio_lengths = zip(*batch) max_audio_len = 0 has_audio = audio_lengths[0] is not None if has_audio: max_audio_len = max(audio_lengths).item() audio_signal = [] for sig, sig_len in batch: if has_audio: sig_len = sig_len.item() if sig_len < max_audio_len: pad = (0, max_audio_len - sig_len) sig = torch.nn.functional.pad(sig, pad) audio_signal.append(sig) if has_audio: audio_signal = torch.stack(audio_signal) audio_lengths = torch.stack(audio_lengths) else: audio_signal, audio_lengths = None, None return audio_signal, audio_lengths # simple data layer to pass buffered frames of audio samples class AudioBuffersDataLayer(IterableDataset): @property def output_types(self): return { "processed_signal": NeuralType(('B', 'D', 'T'), MelSpectrogramType()), "processed_length": NeuralType(tuple('B'), LengthsType()), } def __init__(self): super().__init__() def __iter__(self): return self def __next__(self): if self._buf_count == len(self.signal): raise StopIteration self._buf_count += 1 return ( torch.as_tensor(self.signal[self._buf_count - 1], dtype=torch.float32), torch.as_tensor(self.signal[self._buf_count - 1].shape[1], dtype=torch.int64), ) def set_signal(self, signals): self.signal = signals self.signal_shape = self.signal[0].shape self._buf_count = 0 def __len__(self): return 1 class FeatureFrameBufferer: """ Class to append each feature frame to a buffer and return an array of buffers. """ def __init__(self, asr_model, frame_len=1.6, batch_size=4, total_buffer=4.0, pad_to_buffer_len=True): ''' Args: frame_len: frame's duration, seconds frame_overlap: duration of overlaps before and after current frame, seconds offset: number of symbols to drop for smooth streaming ''' if hasattr(asr_model.preprocessor, 'log') and asr_model.preprocessor.log: self.ZERO_LEVEL_SPEC_DB_VAL = -16.635 # Log-Melspectrogram value for zero signal else: self.ZERO_LEVEL_SPEC_DB_VAL = 0.0 self.asr_model = asr_model self.sr = asr_model._cfg.sample_rate self.frame_len = frame_len timestep_duration = asr_model._cfg.preprocessor.window_stride self.n_frame_len = int(frame_len / timestep_duration) total_buffer_len = int(total_buffer / timestep_duration) self.n_feat = asr_model._cfg.preprocessor.features self.buffer = np.ones([self.n_feat, total_buffer_len], dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL self.pad_to_buffer_len = pad_to_buffer_len self.batch_size = batch_size self.signal_end = False self.frame_reader = None self.feature_buffer_len = total_buffer_len self.feature_buffer = ( np.ones([self.n_feat, self.feature_buffer_len], dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL ) self.frame_buffers = [] self.buffered_features_size = 0 self.reset() self.buffered_len = 0 def reset(self): ''' Reset frame_history and decoder's state ''' self.buffer = np.ones(shape=self.buffer.shape, dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL self.prev_char = '' self.unmerged = [] self.frame_buffers = [] self.buffered_len = 0 self.feature_buffer = ( np.ones([self.n_feat, self.feature_buffer_len], dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL ) def get_batch_frames(self): if self.signal_end: return [] batch_frames = [] for frame in self.frame_reader: batch_frames.append(np.copy(frame)) if len(batch_frames) == self.batch_size: return batch_frames self.signal_end = True return batch_frames def get_frame_buffers(self, frames): # Build buffers for each frame self.frame_buffers = [] for frame in frames: curr_frame_len = frame.shape[1] self.buffered_len += curr_frame_len if curr_frame_len < self.feature_buffer_len and not self.pad_to_buffer_len: self.frame_buffers.append(np.copy(frame)) continue self.buffer[:, :-curr_frame_len] = self.buffer[:, curr_frame_len:] self.buffer[:, -self.n_frame_len :] = frame self.frame_buffers.append(np.copy(self.buffer)) return self.frame_buffers def set_frame_reader(self, frame_reader): self.frame_reader = frame_reader self.signal_end = False def _update_feature_buffer(self, feat_frame): curr_frame_len = feat_frame.shape[1] if curr_frame_len < self.feature_buffer_len and not self.pad_to_buffer_len: self.feature_buffer = np.copy(feat_frame) # assume that only the last frame is less than the buffer length else: self.feature_buffer[:, : -feat_frame.shape[1]] = self.feature_buffer[:, feat_frame.shape[1] :] self.feature_buffer[:, -feat_frame.shape[1] :] = feat_frame self.buffered_features_size += feat_frame.shape[1] def get_norm_consts_per_frame(self, batch_frames): norm_consts = [] for i, frame in enumerate(batch_frames): self._update_feature_buffer(frame) mean_from_buffer = np.mean(self.feature_buffer, axis=1) stdev_from_buffer = np.std(self.feature_buffer, axis=1) norm_consts.append((mean_from_buffer.reshape(self.n_feat, 1), stdev_from_buffer.reshape(self.n_feat, 1))) return norm_consts def normalize_frame_buffers(self, frame_buffers, norm_consts): CONSTANT = 1e-5 for i, frame_buffer in enumerate(frame_buffers): frame_buffers[i] = (frame_buffer - norm_consts[i][0]) / (norm_consts[i][1] + CONSTANT) def get_buffers_batch(self): batch_frames = self.get_batch_frames() while len(batch_frames) > 0: frame_buffers = self.get_frame_buffers(batch_frames) norm_consts = self.get_norm_consts_per_frame(batch_frames) if len(frame_buffers) == 0: continue self.normalize_frame_buffers(frame_buffers, norm_consts) return frame_buffers return [] # class for streaming frame-based ASR # 1) use reset() method to reset FrameASR's state # 2) call transcribe(frame) to do ASR on # contiguous signal's frames class FrameBatchASR: """ class for streaming frame-based ASR use reset() method to reset FrameASR's state call transcribe(frame) to do ASR on contiguous signal's frames """ def __init__( self, asr_model, frame_len=1.6, total_buffer=4.0, batch_size=4, pad_to_buffer_len=True, ): ''' Args: frame_len: frame's duration, seconds frame_overlap: duration of overlaps before and after current frame, seconds offset: number of symbols to drop for smooth streaming ''' self.frame_bufferer = FeatureFrameBufferer( asr_model=asr_model, frame_len=frame_len, batch_size=batch_size, total_buffer=total_buffer, pad_to_buffer_len=pad_to_buffer_len, ) self.asr_model = asr_model self.decoder = getattr(asr_model, "decoder", None) self.batch_size = batch_size self.all_logits = [] self.all_preds = [] self.unmerged = [] if self.decoder is None: self.blank_id = len(asr_model.tokenizer.vocabulary) elif hasattr(asr_model.decoder, "vocabulary"): self.blank_id = len(asr_model.decoder.vocabulary) else: self.blank_id = len(asr_model.joint.vocabulary) self.tokenizer = asr_model.tokenizer self.toks_unmerged = [] self.frame_buffers = [] self.reset() cfg = copy.deepcopy(asr_model._cfg) self.cfg = cfg self.frame_len = frame_len OmegaConf.set_struct(cfg.preprocessor, False) # some changes for streaming scenario cfg.preprocessor.dither = 0.0 cfg.preprocessor.pad_to = 0 cfg.preprocessor.normalize = "None" self.raw_preprocessor = ASRModel.from_config_dict(cfg.preprocessor) self.raw_preprocessor.to(asr_model.device) self.preprocessor = self.raw_preprocessor def reset(self): """ Reset frame_history and decoder's state """ self.prev_char = '' self.unmerged = [] self.data_layer = AudioBuffersDataLayer() self.data_loader = DataLoader(self.data_layer, batch_size=self.batch_size, collate_fn=speech_collate_fn) self.all_logits = [] self.all_preds = [] self.toks_unmerged = [] self.frame_buffers = [] self.frame_bufferer.reset() def read_audio_file(self, audio_filepath: str, delay, model_stride_in_secs): samples = get_samples(audio_filepath) samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) frame_reader = AudioFeatureIterator(samples, self.frame_len, self.raw_preprocessor, self.asr_model.device) self.set_frame_reader(frame_reader) def set_frame_reader(self, frame_reader): self.frame_bufferer.set_frame_reader(frame_reader) @torch.no_grad() def infer_logits(self, keep_logits=False): frame_buffers = self.frame_bufferer.get_buffers_batch() while len(frame_buffers) > 0: self.frame_buffers += frame_buffers[:] self.data_layer.set_signal(frame_buffers[:]) self._get_batch_preds(keep_logits) frame_buffers = self.frame_bufferer.get_buffers_batch() @torch.no_grad() def _get_batch_preds(self, keep_logits=False): device = self.asr_model.device for batch in iter(self.data_loader): feat_signal, feat_signal_len = batch feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) forward_outs = self.asr_model(processed_signal=feat_signal, processed_signal_length=feat_signal_len) if len(forward_outs) == 2: # hybrid ctc rnnt model encoded, encoded_len = forward_outs log_probs = self.asr_model.ctc_decoder(encoder_output=encoded) predictions = log_probs.argmax(dim=-1, keepdim=False) else: log_probs, encoded_len, predictions = forward_outs preds = torch.unbind(predictions) for pred in preds: self.all_preds.append(pred.cpu().numpy()) if keep_logits: log_probs = torch.unbind(log_probs) for log_prob in log_probs: self.all_logits.append(log_prob.cpu()) else: del log_probs del encoded_len del predictions def transcribe(self, tokens_per_chunk: int, delay: int, keep_logits: bool = False): self.infer_logits(keep_logits) self.unmerged = [] for pred in self.all_preds: decoded = pred.tolist() self.unmerged += decoded[len(decoded) - 1 - delay : len(decoded) - 1 - delay + tokens_per_chunk] hypothesis = self.greedy_merge(self.unmerged) if not keep_logits: return hypothesis all_logits = [] for log_prob in self.all_logits: T = log_prob.shape[0] log_prob = log_prob[T - 1 - delay : T - 1 - delay + tokens_per_chunk, :] all_logits.append(log_prob) all_logits = torch.concat(all_logits, 0) return hypothesis, all_logits def greedy_merge(self, preds): decoded_prediction = [] previous = self.blank_id for p in preds: if (p != previous or previous == self.blank_id) and p != self.blank_id: decoded_prediction.append(p) previous = p hypothesis = self.tokenizer.ids_to_text(decoded_prediction) return hypothesis class BatchedFeatureFrameBufferer(FeatureFrameBufferer): """ Batched variant of FeatureFrameBufferer where batch dimension is the independent audio samples. """ def __init__(self, asr_model, frame_len=1.6, batch_size=4, total_buffer=4.0): ''' Args: frame_len: frame's duration, seconds frame_overlap: duration of overlaps before and after current frame, seconds offset: number of symbols to drop for smooth streaming ''' super().__init__(asr_model, frame_len=frame_len, batch_size=batch_size, total_buffer=total_buffer) # OVERRIDES OF BASE CLASS timestep_duration = asr_model._cfg.preprocessor.window_stride total_buffer_len = int(total_buffer / timestep_duration) self.buffer = ( np.ones([batch_size, self.n_feat, total_buffer_len], dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL ) # Preserve list of buffers and indices, one for every sample self.all_frame_reader = [None for _ in range(self.batch_size)] self.signal_end = [False for _ in range(self.batch_size)] self.signal_end_index = [None for _ in range(self.batch_size)] self.buffer_number = 0 # preserve number of buffers returned since reset. self.reset() del self.buffered_len del self.buffered_features_size def reset(self): ''' Reset frame_history and decoder's state ''' super().reset() self.feature_buffer = ( np.ones([self.batch_size, self.n_feat, self.feature_buffer_len], dtype=np.float32) * self.ZERO_LEVEL_SPEC_DB_VAL ) self.all_frame_reader = [None for _ in range(self.batch_size)] self.signal_end = [False for _ in range(self.batch_size)] self.signal_end_index = [None for _ in range(self.batch_size)] self.buffer_number = 0 def get_batch_frames(self): # Exit if all buffers of all samples have been processed if all(self.signal_end): return [] # Otherwise sequentially process frames of each sample one by one. batch_frames = [] for idx, frame_reader in enumerate(self.all_frame_reader): try: frame = next(frame_reader) frame = np.copy(frame) batch_frames.append(frame) except StopIteration: # If this sample has finished all of its buffers # Set its signal_end flag, and assign it the id of which buffer index # did it finish the sample (if not previously set) # This will let the alignment module know which sample in the batch finished # at which index. batch_frames.append(None) self.signal_end[idx] = True if self.signal_end_index[idx] is None: self.signal_end_index[idx] = self.buffer_number self.buffer_number += 1 return batch_frames def get_frame_buffers(self, frames): # Build buffers for each frame self.frame_buffers = [] # Loop over all buffers of all samples for idx in range(self.batch_size): frame = frames[idx] # If the sample has a buffer, then process it as usual if frame is not None: self.buffer[idx, :, : -self.n_frame_len] = self.buffer[idx, :, self.n_frame_len :] self.buffer[idx, :, -self.n_frame_len :] = frame # self.buffered_len += frame.shape[1] # WRAP the buffer at index idx into a outer list self.frame_buffers.append([np.copy(self.buffer[idx])]) else: # If the buffer does not exist, the sample has finished processing # set the entire buffer for that sample to 0 self.buffer[idx, :, :] *= 0.0 self.frame_buffers.append([np.copy(self.buffer[idx])]) return self.frame_buffers def set_frame_reader(self, frame_reader, idx): self.all_frame_reader[idx] = frame_reader self.signal_end[idx] = False self.signal_end_index[idx] = None def _update_feature_buffer(self, feat_frame, idx): # Update the feature buffer for given sample, or reset if the sample has finished processing if feat_frame is not None: self.feature_buffer[idx, :, : -feat_frame.shape[1]] = self.feature_buffer[idx, :, feat_frame.shape[1] :] self.feature_buffer[idx, :, -feat_frame.shape[1] :] = feat_frame # self.buffered_features_size += feat_frame.shape[1] else: self.feature_buffer[idx, :, :] *= 0.0 def get_norm_consts_per_frame(self, batch_frames): for idx, frame in enumerate(batch_frames): self._update_feature_buffer(frame, idx) mean_from_buffer = np.mean(self.feature_buffer, axis=2, keepdims=True) # [B, self.n_feat, 1] stdev_from_buffer = np.std(self.feature_buffer, axis=2, keepdims=True) # [B, self.n_feat, 1] return (mean_from_buffer, stdev_from_buffer) def normalize_frame_buffers(self, frame_buffers, norm_consts): CONSTANT = 1e-8 for i in range(len(frame_buffers)): frame_buffers[i] = (frame_buffers[i] - norm_consts[0][i]) / (norm_consts[1][i] + CONSTANT) def get_buffers_batch(self): batch_frames = self.get_batch_frames() while len(batch_frames) > 0: # while there exists at least one sample that has not been processed yet frame_buffers = self.get_frame_buffers(batch_frames) norm_consts = self.get_norm_consts_per_frame(batch_frames) self.normalize_frame_buffers(frame_buffers, norm_consts) return frame_buffers return [] class BatchedFrameASRRNNT(FrameBatchASR): """ Batched implementation of FrameBatchASR for RNNT models, where the batch dimension is independent audio samples. """ def __init__( self, asr_model, frame_len=1.6, total_buffer=4.0, batch_size=32, max_steps_per_timestep: int = 5, stateful_decoding: bool = False, ): ''' Args: asr_model: An RNNT model. frame_len: frame's duration, seconds. total_buffer: duration of total audio chunk size, in seconds. batch_size: Number of independent audio samples to process at each step. max_steps_per_timestep: Maximum number of tokens (u) to process per acoustic timestep (t). stateful_decoding: Boolean whether to enable stateful decoding for preservation of state across buffers. ''' super().__init__(asr_model, frame_len=frame_len, total_buffer=total_buffer, batch_size=batch_size) # OVERRIDES OF THE BASE CLASS self.max_steps_per_timestep = max_steps_per_timestep self.stateful_decoding = stateful_decoding self.all_alignments = [[] for _ in range(self.batch_size)] self.all_preds = [[] for _ in range(self.batch_size)] self.all_timestamps = [[] for _ in range(self.batch_size)] self.previous_hypotheses = None self.batch_index_map = { idx: idx for idx in range(self.batch_size) } # pointer from global batch id : local sub-batch id try: self.eos_id = self.asr_model.tokenizer.eos_id except Exception: self.eos_id = -1 print("Performing Stateful decoding :", self.stateful_decoding) # OVERRIDES self.frame_bufferer = BatchedFeatureFrameBufferer( asr_model=asr_model, frame_len=frame_len, batch_size=batch_size, total_buffer=total_buffer ) self.reset() def reset(self): """ Reset frame_history and decoder's state """ super().reset() self.all_alignments = [[] for _ in range(self.batch_size)] self.all_preds = [[] for _ in range(self.batch_size)] self.all_timestamps = [[] for _ in range(self.batch_size)] self.previous_hypotheses = None self.batch_index_map = {idx: idx for idx in range(self.batch_size)} self.data_layer = [AudioBuffersDataLayer() for _ in range(self.batch_size)] self.data_loader = [ DataLoader(self.data_layer[idx], batch_size=1, collate_fn=speech_collate_fn) for idx in range(self.batch_size) ] def read_audio_file(self, audio_filepath: list, delay, model_stride_in_secs): assert len(audio_filepath) == self.batch_size # Read in a batch of audio files, one by one for idx in range(self.batch_size): samples = get_samples(audio_filepath[idx]) samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) frame_reader = AudioFeatureIterator(samples, self.frame_len, self.raw_preprocessor, self.asr_model.device) self.set_frame_reader(frame_reader, idx) def set_frame_reader(self, frame_reader, idx): self.frame_bufferer.set_frame_reader(frame_reader, idx) @torch.no_grad() def infer_logits(self): frame_buffers = self.frame_bufferer.get_buffers_batch() while len(frame_buffers) > 0: # While at least 1 sample has a buffer left to process self.frame_buffers += frame_buffers[:] for idx, buffer in enumerate(frame_buffers): self.data_layer[idx].set_signal(buffer[:]) self._get_batch_preds() frame_buffers = self.frame_bufferer.get_buffers_batch() @torch.no_grad() def _get_batch_preds(self): """ Perform dynamic batch size decoding of frame buffers of all samples. Steps: - Load all data loaders of every sample - For all samples, determine if signal has finished. - If so, skip calculation of mel-specs. - If not, compute mel spec and length - Perform Encoder forward over this sub-batch of samples. Maintain the indices of samples that were processed. - If performing stateful decoding, prior to decoder forward, remove the states of samples that were not processed. - Perform Decoder + Joint forward for samples that were processed. - For all output RNNT alignment matrix of the joint do: - If signal has ended previously (this was last buffer of padding), skip alignment - Otherwise, recalculate global index of this sample from the sub-batch index, and preserve alignment. - Same for preds - Update indices of sub-batch with global index map. - Redo steps until all samples were processed (sub-batch size == 0). """ device = self.asr_model.device data_iters = [iter(data_loader) for data_loader in self.data_loader] feat_signals = [] feat_signal_lens = [] new_batch_keys = [] # while not all(self.frame_bufferer.signal_end): for idx in range(self.batch_size): if self.frame_bufferer.signal_end[idx]: continue batch = next(data_iters[idx]) feat_signal, feat_signal_len = batch feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) feat_signals.append(feat_signal) feat_signal_lens.append(feat_signal_len) # preserve batch indeices new_batch_keys.append(idx) if len(feat_signals) == 0: return feat_signal = torch.cat(feat_signals, 0) feat_signal_len = torch.cat(feat_signal_lens, 0) del feat_signals, feat_signal_lens encoded, encoded_len = self.asr_model(processed_signal=feat_signal, processed_signal_length=feat_signal_len) # filter out partial hypotheses from older batch subset if self.stateful_decoding and self.previous_hypotheses is not None: new_prev_hypothesis = [] for new_batch_idx, global_index_key in enumerate(new_batch_keys): old_pos = self.batch_index_map[global_index_key] new_prev_hypothesis.append(self.previous_hypotheses[old_pos]) self.previous_hypotheses = new_prev_hypothesis best_hyp = self.asr_model.decoding.rnnt_decoder_predictions_tensor( encoded, encoded_len, return_hypotheses=True, partial_hypotheses=self.previous_hypotheses ) if self.stateful_decoding: # preserve last state from hypothesis of new batch indices self.previous_hypotheses = best_hyp for idx, hyp in enumerate(best_hyp): global_index_key = new_batch_keys[idx] # get index of this sample in the global batch has_signal_ended = self.frame_bufferer.signal_end[global_index_key] if not has_signal_ended: self.all_alignments[global_index_key].append(hyp.alignments) preds = [hyp.y_sequence for hyp in best_hyp] for idx, pred in enumerate(preds): global_index_key = new_batch_keys[idx] # get index of this sample in the global batch has_signal_ended = self.frame_bufferer.signal_end[global_index_key] if not has_signal_ended: self.all_preds[global_index_key].append(pred.cpu().numpy()) timestamps = [hyp.timestamp for hyp in best_hyp] for idx, timestamp in enumerate(timestamps): global_index_key = new_batch_keys[idx] # get index of this sample in the global batch has_signal_ended = self.frame_bufferer.signal_end[global_index_key] if not has_signal_ended: self.all_timestamps[global_index_key].append(timestamp) if self.stateful_decoding: # State resetting is being done on sub-batch only, global index information is not being updated reset_states = self.asr_model.decoder.initialize_state(encoded) for idx, pred in enumerate(preds): if len(pred) > 0 and pred[-1] == self.eos_id: # reset states : self.previous_hypotheses[idx].y_sequence = self.previous_hypotheses[idx].y_sequence[:-1] self.previous_hypotheses[idx].dec_state = self.asr_model.decoder.batch_select_state( reset_states, idx ) # Position map update if len(new_batch_keys) != len(self.batch_index_map): for new_batch_idx, global_index_key in enumerate(new_batch_keys): self.batch_index_map[global_index_key] = new_batch_idx # let index point from global pos -> local pos del encoded, encoded_len del best_hyp, pred def transcribe( self, tokens_per_chunk: int, delay: int, ): """ Performs "middle token" alignment prediction using the buffered audio chunk. """ self.infer_logits() self.unmerged = [[] for _ in range(self.batch_size)] for idx, alignments in enumerate(self.all_alignments): signal_end_idx = self.frame_bufferer.signal_end_index[idx] if signal_end_idx is None: raise ValueError("Signal did not end") for a_idx, alignment in enumerate(alignments): if delay == len(alignment): # chunk size = buffer size offset = 0 else: # all other cases offset = 1 alignment = alignment[ len(alignment) - offset - delay : len(alignment) - offset - delay + tokens_per_chunk ] ids, toks = self._alignment_decoder(alignment, self.asr_model.tokenizer, self.blank_id) if len(ids) > 0 and a_idx < signal_end_idx: self.unmerged[idx] = inplace_buffer_merge( self.unmerged[idx], ids, delay, model=self.asr_model, ) output = [] for idx in range(self.batch_size): output.append(self.greedy_merge(self.unmerged[idx])) return output def _alignment_decoder(self, alignments, tokenizer, blank_id): s = [] ids = [] for t in range(len(alignments)): for u in range(len(alignments[t])): _, token_id = alignments[t][u] # (logprob, token_id) token_id = int(token_id) if token_id != blank_id: token = tokenizer.ids_to_tokens([token_id])[0] s.append(token) ids.append(token_id) else: # blank token pass return ids, s def greedy_merge(self, preds): decoded_prediction = [p for p in preds] hypothesis = self.asr_model.tokenizer.ids_to_text(decoded_prediction) return hypothesis class BatchedFrameASRTDT(BatchedFrameASRRNNT): """ Batched implementation of FrameBatchASR for TDT models, where the batch dimension is independent audio samples. It's mostly similar to BatchedFrameASRRNNT with special handling of boundary cases due to the frame-skipping resulted by TDT models. """ def __init__( self, asr_model, frame_len=1.6, total_buffer=4.0, batch_size=32, max_steps_per_timestep: int = 5, stateful_decoding: bool = False, tdt_search_boundary: int = 4, ): ''' Args: asr_model: An RNNT model. frame_len: frame's duration, seconds. total_buffer: duration of total audio chunk size, in seconds. batch_size: Number of independent audio samples to process at each step. max_steps_per_timestep: Maximum number of tokens (u) to process per acoustic timestep (t). stateful_decoding: Boolean whether to enable stateful decoding for preservation of state across buffers. tdt_search_boundary: The max number of frames that we search between chunks to match the token at boundary. ''' super().__init__(asr_model, frame_len=frame_len, total_buffer=total_buffer, batch_size=batch_size) self.tdt_search_boundary = tdt_search_boundary def transcribe( self, tokens_per_chunk: int, delay: int, ): """ Performs "middle token" alignment prediction using the buffered audio chunk. """ self.infer_logits() self.unmerged = [[] for _ in range(self.batch_size)] for idx, alignments in enumerate(self.all_alignments): signal_end_idx = self.frame_bufferer.signal_end_index[idx] if signal_end_idx is None: raise ValueError("Signal did not end") for a_idx, alignment in enumerate(alignments): if delay == len(alignment): # chunk size = buffer size offset = 0 else: # all other cases offset = 1 longer_alignment = alignment[ len(alignment) - offset - delay - self.tdt_search_boundary : len(alignment) - offset - delay + tokens_per_chunk ] alignment = alignment[ len(alignment) - offset - delay : len(alignment) - offset - delay + tokens_per_chunk ] longer_ids, longer_toks = self._alignment_decoder( longer_alignment, self.asr_model.tokenizer, self.blank_id ) ids, _ = self._alignment_decoder(alignment, self.asr_model.tokenizer, self.blank_id) if len(longer_ids) > 0 and a_idx < signal_end_idx: if a_idx == 0 or len(self.unmerged[idx]) == 0: self.unmerged[idx] = inplace_buffer_merge( self.unmerged[idx], ids, delay, model=self.asr_model, ) elif len(self.unmerged[idx]) > 0 and len(longer_toks) > 1: id_to_match = self.unmerged[idx][-1] start = min(len(longer_ids) - len(ids), len(longer_ids) - 1) end = -1 for i in range(start, end, -1): if longer_ids[i] == id_to_match: ids = longer_ids[i + 1 :] break self.unmerged[idx] = inplace_buffer_merge( self.unmerged[idx], ids, delay, model=self.asr_model, ) output = [] for idx in range(self.batch_size): output.append(self.greedy_merge(self.unmerged[idx])) return output class LongestCommonSubsequenceBatchedFrameASRRNNT(BatchedFrameASRRNNT): """ Implements a token alignment algorithm for text alignment instead of middle token alignment. For more detail, read the docstring of longest_common_subsequence_merge(). """ def __init__( self, asr_model, frame_len=1.6, total_buffer=4.0, batch_size=4, max_steps_per_timestep: int = 5, stateful_decoding: bool = False, alignment_basepath: str = None, ): ''' Args: asr_model: An RNNT model. frame_len: frame's duration, seconds. total_buffer: duration of total audio chunk size, in seconds. batch_size: Number of independent audio samples to process at each step. max_steps_per_timestep: Maximum number of tokens (u) to process per acoustic timestep (t). stateful_decoding: Boolean whether to enable stateful decoding for preservation of state across buffers. alignment_basepath: Str path to a directory where alignments from LCS will be preserved for later analysis. ''' super().__init__(asr_model, frame_len, total_buffer, batch_size, max_steps_per_timestep, stateful_decoding) self.sample_offset = 0 self.lcs_delay = -1 self.alignment_basepath = alignment_basepath def transcribe( self, tokens_per_chunk: int, delay: int, ): if self.lcs_delay < 0: raise ValueError( "Please set LCS Delay valus as `(buffer_duration - chunk_duration) / model_stride_in_secs`" ) self.infer_logits() self.unmerged = [[] for _ in range(self.batch_size)] for idx, alignments in enumerate(self.all_alignments): signal_end_idx = self.frame_bufferer.signal_end_index[idx] if signal_end_idx is None: raise ValueError("Signal did not end") for a_idx, alignment in enumerate(alignments): # Middle token first chunk if a_idx == 0: # len(alignment) - 1 - delay + tokens_per_chunk alignment = alignment[len(alignment) - 1 - delay :] ids, toks = self._alignment_decoder(alignment, self.asr_model.tokenizer, self.blank_id) if len(ids) > 0: self.unmerged[idx] = inplace_buffer_merge( self.unmerged[idx], ids, delay, model=self.asr_model, ) else: ids, toks = self._alignment_decoder(alignment, self.asr_model.tokenizer, self.blank_id) if len(ids) > 0 and a_idx < signal_end_idx: if self.alignment_basepath is not None: basepath = self.alignment_basepath sample_offset = self.sample_offset + idx alignment_offset = a_idx path = os.path.join(basepath, str(sample_offset)) os.makedirs(path, exist_ok=True) path = os.path.join(path, "alignment_" + str(alignment_offset) + '.pt') filepath = path else: filepath = None self.unmerged[idx] = lcs_alignment_merge_buffer( self.unmerged[idx], ids, self.lcs_delay, model=self.asr_model, max_steps_per_timestep=self.max_steps_per_timestep, filepath=filepath, ) output = [] for idx in range(self.batch_size): output.append(self.greedy_merge(self.unmerged[idx])) return output class CacheAwareStreamingAudioBuffer: """ A buffer to be used for cache-aware streaming. It can load a single or multiple audio files/processed signals, split them in chunks and return one on one. It can be used to simulate streaming audio or audios. """ def __init__(self, model, online_normalization=None, pad_and_drop_preencoded=False): ''' Args: model: An ASR model. online_normalization (bool): whether to perform online normalization per chunk or normalize the whole audio before chunking pad_and_drop_preencoded (bool): if true pad first audio chunk and always drop preencoded ''' self.model = model self.buffer = None self.buffer_idx = 0 self.streams_length = None self.step = 0 self.pad_and_drop_preencoded = pad_and_drop_preencoded self.online_normalization = online_normalization if not isinstance(model.encoder, StreamingEncoder): raise ValueError( "The model's encoder is not inherited from StreamingEncoder, and likely not to support streaming!" ) if model.encoder.streaming_cfg is None: model.encoder.setup_streaming_params() self.streaming_cfg = model.encoder.streaming_cfg self.input_features = model.encoder._feat_in self.preprocessor = self.extract_preprocessor() if hasattr(model.encoder, "pre_encode") and hasattr(model.encoder.pre_encode, "get_sampling_frames"): self.sampling_frames = model.encoder.pre_encode.get_sampling_frames() else: self.sampling_frames = None def __iter__(self): while True: if self.buffer_idx >= self.buffer.size(-1): return if self.buffer_idx == 0 and isinstance(self.streaming_cfg.chunk_size, list): if self.pad_and_drop_preencoded: chunk_size = self.streaming_cfg.chunk_size[1] else: chunk_size = self.streaming_cfg.chunk_size[0] else: chunk_size = ( self.streaming_cfg.chunk_size[1] if isinstance(self.streaming_cfg.chunk_size, list) else self.streaming_cfg.chunk_size ) if self.buffer_idx == 0 and isinstance(self.streaming_cfg.shift_size, list): if self.pad_and_drop_preencoded: shift_size = self.streaming_cfg.shift_size[1] else: shift_size = self.streaming_cfg.shift_size[0] else: shift_size = ( self.streaming_cfg.shift_size[1] if isinstance(self.streaming_cfg.shift_size, list) else self.streaming_cfg.shift_size ) audio_chunk = self.buffer[:, :, self.buffer_idx : self.buffer_idx + chunk_size] if self.sampling_frames is not None: # checking to make sure the audio chunk has enough frames to produce at least one output after # downsampling if self.buffer_idx == 0 and isinstance(self.sampling_frames, list): cur_sampling_frames = self.sampling_frames[0] else: cur_sampling_frames = ( self.sampling_frames[1] if isinstance(self.sampling_frames, list) else self.sampling_frames ) if audio_chunk.size(-1) < cur_sampling_frames: return # Adding the cache needed for the pre-encoder part of the model to the chunk # if there is not enough frames to be used as the pre-encoding cache, zeros would be added zeros_pads = None if self.buffer_idx == 0 and isinstance(self.streaming_cfg.pre_encode_cache_size, list): if self.pad_and_drop_preencoded: cache_pre_encode_num_frames = self.streaming_cfg.pre_encode_cache_size[1] else: cache_pre_encode_num_frames = self.streaming_cfg.pre_encode_cache_size[0] cache_pre_encode = torch.zeros( (audio_chunk.size(0), self.input_features, cache_pre_encode_num_frames), device=audio_chunk.device, dtype=audio_chunk.dtype, ) else: if isinstance(self.streaming_cfg.pre_encode_cache_size, list): pre_encode_cache_size = self.streaming_cfg.pre_encode_cache_size[1] else: pre_encode_cache_size = self.streaming_cfg.pre_encode_cache_size start_pre_encode_cache = self.buffer_idx - pre_encode_cache_size if start_pre_encode_cache < 0: start_pre_encode_cache = 0 cache_pre_encode = self.buffer[:, :, start_pre_encode_cache : self.buffer_idx] if cache_pre_encode.size(-1) < pre_encode_cache_size: zeros_pads = torch.zeros( ( audio_chunk.size(0), audio_chunk.size(-2), pre_encode_cache_size - cache_pre_encode.size(-1), ), device=audio_chunk.device, dtype=audio_chunk.dtype, ) added_len = cache_pre_encode.size(-1) audio_chunk = torch.cat((cache_pre_encode, audio_chunk), dim=-1) if self.online_normalization: audio_chunk, x_mean, x_std = normalize_batch( x=audio_chunk, seq_len=torch.tensor([audio_chunk.size(-1)] * audio_chunk.size(0)), normalize_type=self.model_normalize_type, ) if zeros_pads is not None: # TODO: check here when zero_pads is not None and added_len is already non-zero audio_chunk = torch.cat((zeros_pads, audio_chunk), dim=-1) added_len += zeros_pads.size(-1) max_chunk_lengths = self.streams_length - self.buffer_idx max_chunk_lengths = max_chunk_lengths + added_len chunk_lengths = torch.clamp(max_chunk_lengths, min=0, max=audio_chunk.size(-1)) self.buffer_idx += shift_size self.step += 1 yield audio_chunk, chunk_lengths def is_buffer_empty(self): if self.buffer_idx >= self.buffer.size(-1): return True else: return False def __len__(self): return len(self.buffer) def reset_buffer(self): self.buffer = None self.buffer_idx = 0 self.streams_length = None self.step = 0 def reset_buffer_pointer(self): self.buffer_idx = 0 self.step = 0 def extract_preprocessor(self): cfg = copy.deepcopy(self.model._cfg) self.model_normalize_type = cfg.preprocessor.normalize OmegaConf.set_struct(cfg.preprocessor, False) cfg.preprocessor.dither = 0.0 cfg.preprocessor.pad_to = 0 if self.online_normalization: cfg.preprocessor.normalize = "None" preprocessor = self.model.from_config_dict(cfg.preprocessor) return preprocessor.to(self.get_model_device()) def append_audio_file(self, audio_filepath, stream_id=-1): audio = get_samples(audio_filepath) processed_signal, processed_signal_length, stream_id = self.append_audio(audio, stream_id) return processed_signal, processed_signal_length, stream_id def append_audio(self, audio, stream_id=-1): processed_signal, processed_signal_length = self.preprocess_audio(audio) processed_signal, processed_signal_length, stream_id = self.append_processed_signal( processed_signal, stream_id ) return processed_signal, processed_signal_length, stream_id def append_processed_signal(self, processed_signal, stream_id=-1): processed_signal_length = torch.tensor(processed_signal.size(-1), device=processed_signal.device) if stream_id >= 0 and (self.streams_length is not None and stream_id >= len(self.streams_length)): raise ValueError("Not valid stream_id!") if self.buffer is None: if stream_id >= 0: raise ValueError("stream_id can not be specified when there is no stream.") self.buffer = processed_signal self.streams_length = torch.tensor([processed_signal_length], device=processed_signal.device) else: if self.buffer.size(1) != processed_signal.size(1): raise ValueError("Buffer and the processed signal have different dimensions!") if stream_id < 0: self.buffer = torch.nn.functional.pad(self.buffer, pad=(0, 0, 0, 0, 0, 1)) self.streams_length = torch.cat( (self.streams_length, torch.tensor([0], device=self.streams_length.device)), dim=-1 ) stream_id = len(self.streams_length) - 1 needed_len = self.streams_length[stream_id] + processed_signal_length if needed_len > self.buffer.size(-1): self.buffer = torch.nn.functional.pad(self.buffer, pad=(0, needed_len - self.buffer.size(-1))) self.buffer[ stream_id, :, self.streams_length[stream_id] : self.streams_length[stream_id] + processed_signal_length ] = processed_signal self.streams_length[stream_id] = self.streams_length[stream_id] + processed_signal.size(-1) if self.online_normalization: processed_signal, x_mean, x_std = normalize_batch( x=processed_signal, seq_len=torch.tensor([processed_signal_length]), normalize_type=self.model_normalize_type, ) return processed_signal, processed_signal_length, stream_id def get_model_device(self): return self.model.device def preprocess_audio(self, audio, device=None): if device is None: device = self.get_model_device() audio_signal = torch.from_numpy(audio).unsqueeze_(0).to(device) audio_signal_len = torch.Tensor([audio.shape[0]]).to(device) processed_signal, processed_signal_length = self.preprocessor( input_signal=audio_signal, length=audio_signal_len ) return processed_signal, processed_signal_length def get_all_audios(self): processed_signal = self.buffer if self.online_normalization: processed_signal, x_mean, x_std = normalize_batch( x=processed_signal, seq_len=torch.tensor(self.streams_length), normalize_type=self.model_normalize_type, ) return processed_signal, self.streams_length class FrameBatchMultiTaskAED(FrameBatchASR): def __init__(self, asr_model, frame_len=4, total_buffer=4, batch_size=4): super().__init__(asr_model, frame_len, total_buffer, batch_size, pad_to_buffer_len=False) self.window_stride = asr_model._cfg.preprocessor.window_stride self.subsampling_factor = asr_model._cfg.encoder.subsampling_factor self.chunk_offsets = [ 0, ] # chunk offsets in terms of num frames before subsampling def reset(self): super().reset() self.chunk_offsets = [ 0, ] def get_input_tokens(self, sample: dict): if self.asr_model.prompt_format == "canary": expected_slots = {"source_lang", "target_lang", "taskname", "pnc"} default_slot_values = {} elif self.asr_model.prompt_format == "canary2": expected_slots = {"source_lang", "target_lang"} default_slot_values = { "decodercontext": "", "emotion": "<|emo:undefined|>", "itn": "<|noitn|>", "timestamp": "<|notimestamp|>", "diarize": "<|nodiarize|>", "pnc": "<|pnc|>", # consistent with canary1 } else: raise ValueError(f"Unknown prompt format: {self.asr_model.prompt_format}") missing_keys = [k for k in expected_slots if k not in sample] if missing_keys: raise RuntimeError( f"We found sample that is missing the following keys: {missing_keys}" f"Please ensure that every utterance in the input manifests contains these keys. Sample: {sample}" ) # fill optional slots for k, v in default_slot_values.items(): sample[k] = sample.get(k, v) tokens = self.asr_model.prompt.encode_dialog( turns=[ { "role": "user", "slots": { **sample, self.asr_model.prompt.PROMPT_LANGUAGE_SLOT: "spl_tokens", }, } ] )["context_ids"] return torch.tensor(tokens, dtype=torch.long, device=self.asr_model.device).unsqueeze(0) # [1, T] def read_audio_file(self, audio_filepath: str, delay, model_stride_in_secs, meta_data): self.input_tokens = self.get_input_tokens(meta_data) samples = get_samples(audio_filepath) samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) frame_reader = AudioFeatureIterator( samples, self.frame_len, self.raw_preprocessor, self.asr_model.device, pad_to_frame_len=False ) self.set_frame_reader(frame_reader) @torch.no_grad() def _get_batch_preds(self, keep_logits=False): device = self.asr_model.device for batch in iter(self.data_loader): feat_signal, feat_signal_len = batch # keep track of chunk offsets self.chunk_offsets.extend(feat_signal_len.tolist()) feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) tokens = self.input_tokens.to(device).repeat(feat_signal.size(0), 1) tokens_len = torch.tensor([tokens.size(1)] * tokens.size(0), device=device).long() batch_input = PromptedAudioToTextMiniBatch( audio=feat_signal, audio_lens=feat_signal_len, transcript=None, transcript_lens=None, prompt=tokens, prompt_lens=tokens_len, prompted_transcript=None, prompted_transcript_lens=None, ) predictions = self.asr_model.predict_step(batch_input, has_processed_signal=True) self.all_preds.extend(predictions) del predictions def transcribe( self, tokens_per_chunk: Optional[int] = None, delay: Optional[int] = None, keep_logits: bool = False ): """ unsued params are for keeping the same signature as the parent class """ self.infer_logits(keep_logits) # join hypotheses hypothesis = self._join_hypotheses(self.all_preds) if not keep_logits: return hypothesis print("keep_logits=True is not supported for MultiTaskAEDFrameBatchInfer. Returning empty logits.") return hypothesis, [] def _join_hypotheses(self, hypotheses): if len(hypotheses) == 1: return hypotheses[0] # initialize a new hypothesis merged_hypthesis = rnnt_utils.Hypothesis( score=0.0, y_sequence=torch.tensor([]), timestamp={ 'char': [], 'word': [], 'segment': [], }, ) # join merged_hypthesis = self._join_text(merged_hypthesis, hypotheses) merged_hypthesis = self._join_y_sequence(merged_hypthesis, hypotheses) merged_hypthesis = self._join_timestamp(merged_hypthesis, hypotheses) return merged_hypthesis def _join_text(self, merged_hypothesis, hypotheses): merged_hypothesis.text = " ".join([h.text for h in hypotheses]) return merged_hypothesis def _join_y_sequence(self, merged_hypothesis, hypotheses): merged_hypothesis.y_sequence = torch.cat([h.y_sequence for h in hypotheses]) return merged_hypothesis def _join_timestamp(self, merged_hypothesis, hypotheses): # word level cumulative_offset = 0 for i, h in enumerate(hypotheses): cumulative_offset += self.chunk_offsets[i] # self.chunk_offsets starts with 0, # update frame numbers updated_timestamps = [ { **word, 'start_offset': word['start_offset'] + cumulative_offset // self.subsampling_factor, # dividing here to avoid error accumulation over long audios 'end_offset': word['end_offset'] + cumulative_offset // self.subsampling_factor, } for word in h.timestamp['word'] ] # update times updated_timestamps = [ { **word, 'start': word['start_offset'] * self.window_stride * self.subsampling_factor, 'end': word['end_offset'] * self.window_stride * self.subsampling_factor, } for word in updated_timestamps ] merged_hypothesis.timestamp['word'].extend(updated_timestamps) # segment level cumulative_offset = 0 for i, h in enumerate(hypotheses): cumulative_offset += self.chunk_offsets[i] # update frame numbers updated_timestamps = [ { **segment, 'start_offset': segment['start_offset'] + cumulative_offset // self.subsampling_factor, 'end_offset': segment['end_offset'] + cumulative_offset // self.subsampling_factor, } for segment in h.timestamp['segment'] ] # update times updated_timestamps = [ { **segment, 'start': segment['start_offset'] * self.window_stride * self.subsampling_factor, 'end': segment['end_offset'] * self.window_stride * self.subsampling_factor, } for segment in updated_timestamps ] merged_hypothesis.timestamp['segment'].extend(updated_timestamps) return merged_hypothesis class FrameBatchChunkedRNNT(FrameBatchASR): def __init__(self, asr_model, frame_len=4, total_buffer=4, batch_size=4): super().__init__(asr_model, frame_len, total_buffer, batch_size, pad_to_buffer_len=False) def read_audio_file(self, audio_filepath: str, delay, model_stride_in_secs): samples = get_samples(audio_filepath) samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) frame_reader = AudioFeatureIterator( samples, self.frame_len, self.raw_preprocessor, self.asr_model.device, pad_to_frame_len=False ) self.set_frame_reader(frame_reader) @torch.no_grad() def _get_batch_preds(self, keep_logits=False): device = self.asr_model.device for batch in iter(self.data_loader): feat_signal, feat_signal_len = batch feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) encoded, encoded_len = self.asr_model( processed_signal=feat_signal, processed_signal_length=feat_signal_len ) best_hyp_text, all_hyp_text = self.asr_model.decoding.rnnt_decoder_predictions_tensor( encoder_output=encoded, encoded_lengths=encoded_len, return_hypotheses=False ) self.all_preds.extend(best_hyp_text) del best_hyp_text del all_hyp_text del encoded del encoded_len def transcribe( self, tokens_per_chunk: Optional[int] = None, delay: Optional[int] = None, keep_logits: bool = False ): """ unsued params are for keeping the same signature as the parent class """ self.infer_logits(keep_logits) hypothesis = " ".join(self.all_preds) if not keep_logits: return hypothesis print("keep_logits=True is not supported for FrameBatchChunkedRNNT. Returning empty logits.") return hypothesis, [] class FrameBatchChunkedCTC(FrameBatchASR): def __init__(self, asr_model, frame_len=4, total_buffer=4, batch_size=4): super().__init__(asr_model, frame_len, total_buffer, batch_size, pad_to_buffer_len=False) def read_audio_file(self, audio_filepath: str, delay, model_stride_in_secs): samples = get_samples(audio_filepath) samples = np.pad(samples, (0, int(delay * model_stride_in_secs * self.asr_model._cfg.sample_rate))) frame_reader = AudioFeatureIterator( samples, self.frame_len, self.raw_preprocessor, self.asr_model.device, pad_to_frame_len=False ) self.set_frame_reader(frame_reader) @torch.no_grad() def _get_batch_preds(self, keep_logits=False): device = self.asr_model.device for batch in iter(self.data_loader): feat_signal, feat_signal_len = batch feat_signal, feat_signal_len = feat_signal.to(device), feat_signal_len.to(device) results = self.asr_model(processed_signal=feat_signal, processed_signal_length=feat_signal_len) if len(results) == 2: # hybrid model encoded, encoded_len = results log_probs = self.asr_model.ctc_decoder(encoder_output=encoded) transcribed_texts, _ = self.asr_model.ctc_decoding.ctc_decoder_predictions_tensor( decoder_outputs=log_probs, decoder_lengths=encoded_len, return_hypotheses=False, ) else: log_probs, encoded_len, predictions = results transcribed_texts, _ = self.asr_model.decoding.ctc_decoder_predictions_tensor( decoder_outputs=log_probs, decoder_lengths=encoded_len, return_hypotheses=False, ) self.all_preds.extend(transcribed_texts) del log_probs del encoded_len del predictions def transcribe( self, tokens_per_chunk: Optional[int] = None, delay: Optional[int] = None, keep_logits: bool = False ): """ unsued params are for keeping the same signature as the parent class """ self.infer_logits(keep_logits) hypothesis = " ".join(self.all_preds) if not keep_logits: return hypothesis print("keep_logits=True is not supported for FrameBatchChunkedCTC. Returning empty logits.") return hypothesis, [] @dataclass class ContextSize: left: int chunk: int right: int def total(self) -> int: """Total context size""" return self.left + self.chunk + self.right def subsample(self, factor: int) -> "ContextSize": """ Subsample context size by factor Args: factor: subsampling factor """ return ContextSize( left=self.left // factor, chunk=self.chunk // factor, right=self.right // factor, ) def add_frames_get_removed_(self, num_frames: int, is_last_chunk: bool, expected_context: "ContextSize") -> int: """ Add frames to context size Args: num_frames: number of frames to add is_last_chunk: if last chunk Returns: number of frames removed from the left side """ if num_frames > expected_context.chunk + expected_context.right: raise ValueError( f"Added chunk length {num_frames} is larger " f"than expected chunk with right context {expected_context}" ) # consider first everything is moved to right/left context, then move to chunk self.left += self.chunk self.chunk = 0 self.right += num_frames if is_last_chunk: # move all samples to chunk, empty right part self.chunk = self.right self.right = 0 else: self.chunk = expected_context.chunk self.right -= expected_context.chunk extra_samples = max(self.total() - expected_context.total(), 0) self.left -= extra_samples if not is_last_chunk: assert self.right == expected_context.right return extra_samples def __str__(self): return f"Left {self.left} - Chunk {self.chunk} - Right {self.right}" @dataclass class ContextSizeBatch: """Batched context size""" left: torch.Tensor chunk: torch.Tensor right: torch.Tensor def total(self) -> torch.Tensor: """Total context size""" return self.left + self.chunk + self.right def subsample(self, factor: int) -> "ContextSizeBatch": """ Subsample context size by factor Args: factor: subsampling factor """ return ContextSizeBatch( left=torch.div(self.left, factor, rounding_mode="floor"), chunk=torch.div(self.chunk, factor, rounding_mode="floor"), right=torch.div(self.right, factor, rounding_mode="floor"), ) def add_frames_( self, num_frames_batch: torch.Tensor, is_last_chunk_batch: torch.Tensor, expected_context: "ContextSize" ): """ Add frames to context size Args: num_frames_batch: number of frames to add is_last_chunk_batch: if last chunk Returns: number of frames removed from the left side """ self.left += self.chunk self.chunk.fill_(0) self.right += num_frames_batch self.chunk = torch.where(is_last_chunk_batch, self.right, expected_context.chunk) self.right = torch.where(is_last_chunk_batch, 0, self.right - expected_context.chunk) # fix left context self.left = torch.where(self.chunk > 0, self.left, 0) extra_samples = torch.maximum(self.total() - expected_context.total(), torch.zeros_like(self.left)) self.left -= extra_samples self.left = torch.where(self.left < 0, torch.zeros_like(self.left), self.left) class StreamingBatchedAudioBuffer: """Batched audio buffer with strict context management for streaming inference without left padding.""" def __init__(self, batch_size: int, context_samples: ContextSize, dtype: torch.dtype, device: torch.device | str): """ Init batched audio buffer for streaming inference Args: batch_size: batch size context_samples: context size dtype: buffer dtype device: device for buffer """ self.batch_size = batch_size self.expected_context = context_samples self.samples = torch.zeros([batch_size, 0], dtype=dtype, device=device) self.context_size = ContextSize(left=0, chunk=0, right=0) self.context_size_batch = ContextSizeBatch( left=torch.zeros([batch_size], dtype=torch.long, device=device), chunk=torch.zeros([batch_size], dtype=torch.long, device=device), right=torch.zeros([batch_size], dtype=torch.long, device=device), ) def add_audio_batch_( self, audio_batch: torch.Tensor, audio_lengths: torch.Tensor, is_last_chunk: bool, is_last_chunk_batch: torch.Tensor, ): """ Add audio batch to buffer Args: audio_batch: chunk with audio audio_lengths: length of audio is_last_chunk: if last chunk is_last_chunk_batch: if last chunk for each audio utterance """ added_chunk_length = audio_batch.shape[1] # concat new chunk with buffer, remove extra samples self.samples = torch.cat((self.samples, audio_batch), dim=1) extra_samples_in_buffer = self.context_size.add_frames_get_removed_( added_chunk_length, is_last_chunk=is_last_chunk, expected_context=self.expected_context ) self.context_size_batch.add_frames_( num_frames_batch=audio_lengths, is_last_chunk_batch=is_last_chunk_batch, expected_context=self.expected_context, ) # leave only full_ctx_audio_samples in buffer if extra_samples_in_buffer > 0: self.samples = self.samples[:, extra_samples_in_buffer:].clone() def load_audio(file_path: str | Path, sample_rate: int = 16000) -> tuple[torch.Tensor, int]: """Load audio from file""" audio, sr = librosa.load(file_path, sr=sample_rate) return torch.tensor(audio, dtype=torch.float32), sr class AudioBatch(NamedTuple): audio_signals: torch.Tensor audio_signal_lengths: torch.Tensor @staticmethod def collate_fn( audio_batch: list[torch.Tensor], ) -> "AudioBatch": """ Collate audio signals to batch """ audio_signals = pad_sequence( [audio_tensor for audio_tensor in audio_batch], batch_first=True, padding_value=0.0 ) audio_signal_lengths = torch.tensor([audio_tensor.shape[0] for audio_tensor in audio_batch]).long() return AudioBatch( audio_signals=audio_signals, audio_signal_lengths=audio_signal_lengths, ) class SimpleAudioDataset(Dataset): """Dataset constructed from audio filenames. Each item - audio""" def __init__(self, audio_filenames: list[str | Path], sample_rate: int = 16000): super().__init__() self.audio_filenames = audio_filenames self.sample_rate = sample_rate def __getitem__(self, item: int) -> torch.Tensor: audio, _ = load_audio(self.audio_filenames[item]) return audio def __len__(self): return len(self.audio_filenames)