pipeline.py

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 math

import jax
import jax.numpy as jnp
import numpy as np
import requests
from flax import jax_utils
from flax.core.frozen_dict import freeze
from flax.training.common_utils import shard
from jax.sharding import PartitionSpec as P
from transformers import WhisperProcessor, is_tokenizers_available, WhisperFeatureExtractor, WhisperTokenizerFast
from transformers.models.whisper.tokenization_whisper import TO_LANGUAGE_CODE, WhisperTokenizer
from transformers.pipelines.audio_utils import ffmpeg_read
from transformers.utils import logging

from .modeling_flax_whisper import FlaxWhisperForConditionalGeneration
from .partitioner import PjitPartitioner
from .train_state import InferenceState


logger = logging.get_logger(__name__)

# 2D parameter and activation partitioning for DP
logical_axis_rules_dp = (
    ("batch", "data"),
    ("mlp", None),
    ("heads", None),
    ("vocab", None),
    ("embed", None),
    ("embed", None),
    ("joined_kv", None),
    ("kv", None),
    ("length", None),
    ("num_mel", None),
    ("channels", None),
)


class FlaxWhisperPipline:
    def __init__(
        self,
        checkpoint="openai/whisper-large-v2",
        dtype=jnp.float32,
        batch_size=None,
        max_length=None,
    ):
        """
        Args
            checkpoint (`str`, *optional*, defaults to `"openai/whisper-large-v2"):
                The Whisper checkpoint to use with the pipeline. Must be an available checkpoint on the Hugging Face Hub
                with Flax weights.
            dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
                The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
                `jax.numpy.bfloat16` (on TPUs). This can be used to enable half-precision inference on GPUs or TPUs.
                If specified all the computation will be performed with the given `dtype`. **Note that this only
                specifies the dtype of the computation and does not influence the dtype of model parameters.**
            batch_size (`int`, *optional*, defaults to the minimum per-device batch size, i.e. `jax.local_device_count()`):
                The batch size to be used in chunking transcription. Beneficial for transcribing long audio files. Passing
                a batch size in the `__init__` method will be superseded by any batch size passed to the `__call__` method.
            max_length (`int`, *optional*):
                The maximum numbers of tokens to generate. Defaults to `model.config.max_length`.
        """
        self.checkpoint = checkpoint
        self.dtype = dtype

        self.processor = WhisperProcessor.from_pretrained(self.checkpoint)
        self.feature_extractor = self.processor.feature_extractor
        # potentially load fast tokenizer if available
        tokenizer_cls = WhisperTokenizerFast if is_tokenizers_available() else WhisperTokenizer
        self.tokenizer = tokenizer_cls.from_pretrained(checkpoint)

        self.model, self.params = FlaxWhisperForConditionalGeneration.from_pretrained(
            self.checkpoint,
            _do_init=False,
            dtype=self.dtype,
        )

        self.max_length = max_length if max_length is not None else self.model.generation_config.max_length
        self.min_batch_size = jax.local_device_count()
        self.batch_size = (
            batch_size if batch_size is not None else self.min_batch_size
        )  # we need a minimum of 1 batch per-device

        def generate(params, input_features, forced_decoder_ids, return_timestamps):
            output_ids = self.model.pipeline_generate(
                input_features,
                params=params,
                forced_decoder_ids=forced_decoder_ids,
                return_timestamps=return_timestamps,
                max_length=self.max_length,
            )
            return output_ids

        # use pmap for DP by default - this is compatible on a Colab TPU v2
        self.params = jax_utils.replicate(self.params)
        self.p_generate = jax.pmap(
            generate, "input_features", in_axes=(0, 0, None), out_axes=0, static_broadcasted_argnums=(3,)
        )
        self.is_sharded = False

    def shard_params(self, num_mp_partitions=1, logical_axis_rules=logical_axis_rules_dp):
        def init_fn():
            input_shape = (1, self.model.config.num_mel_bins, 2 * self.model.config.max_source_positions)

            input_features = jnp.zeros(input_shape, dtype="f4")
            input_features = input_features.at[(..., -1)].set(self.model.config.eos_token_id)

            decoder_input_ids = jnp.zeros((input_shape[0], 1), dtype="i4")
            decoder_attention_mask = jnp.ones_like(decoder_input_ids)

            batch_size, sequence_length = decoder_input_ids.shape
            decoder_position_ids = jnp.broadcast_to(
                jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
            )

            rng = jax.random.PRNGKey(0)
            init_params = self.model.module.init(
                rng,
                input_features=input_features,
                decoder_input_ids=decoder_input_ids,
                decoder_attention_mask=decoder_attention_mask,
                decoder_position_ids=decoder_position_ids,
                return_dict=False,
            )
            return init_params

        # Axis names metadata
        param_axes = jax.eval_shape(init_fn)["params_axes"]

        # Create InferenceState, since the partitioner expects it
        state = InferenceState(
            step=jnp.array(0),
            params=freeze(self.model.params_shape_tree),
            params_axes=freeze(param_axes),
            flax_mutables=None,
            flax_mutables_axes=param_axes,
        )

        partitioner = PjitPartitioner(num_partitions=num_mp_partitions, logical_axis_rules=logical_axis_rules)

        mesh_axes = partitioner.get_mesh_axes(state)
        params_spec = mesh_axes.params

        p_shard_params = partitioner.partition(self.model.to_bf16, (params_spec,), params_spec)

        # This will auto-magically run in mesh context
        self.params = p_shard_params(freeze(jax_utils.unreplicate(self.params)))
        self.is_sharded = True

        def generate(params, input_features, forced_decoder_ids, return_timestamps):
            output_ids = self.model.pipeline_generate(
                input_features,
                params=params,
                forced_decoder_ids=forced_decoder_ids,
                return_timestamps=return_timestamps,
                max_length=self.max_length,
            )
            return output_ids

        # Use pjit for generate only once we've sharded the params
        self.p_generate = partitioner.partition(
            generate,
            in_axis_resources=(params_spec, P("data"), None),
            out_axis_resources=P("data"),
            static_argnums=(3,),
        )

    def generate(self, input_features, language=None, task=None, return_timestamps=False):
        forced_decoder_ids = self.get_forced_decoder_ids(
            language=language, task=task, return_timestamps=return_timestamps
        )
        if not self.is_sharded:
            # if we're using pmap we need to manually replicate the input data across devices and gather the output tokens
            output_ids = self.p_generate(
                freeze(self.params), shard(input_features), forced_decoder_ids, return_timestamps
            ).sequences
            output_ids = jax.device_get(output_ids.reshape(-1, self.max_length))
        else:
            # pjit handles replication / gathering for us auto-magically
            output_ids = self.p_generate(
                freeze(self.params), input_features, forced_decoder_ids, return_timestamps
            ).sequences
        return output_ids

    def get_forced_decoder_ids(self, generation_config=None, task=None, language=None, return_timestamps=False):
        if generation_config is None:
            generation_config = self.model.generation_config

        if hasattr(generation_config, "is_multilingual"):
            is_multilingual = generation_config.is_multilingual
        else:
            is_multilingual = None

        forced_decoder_ids = []

        if is_multilingual:
            if language is not None:
                language = language.lower()
                if language in generation_config.lang_to_id.keys():
                    language_token = language
                elif language in TO_LANGUAGE_CODE.values():
                    language_token = f"<|{language}|>"
                elif language in TO_LANGUAGE_CODE.keys():
                    language_token = f"<|{TO_LANGUAGE_CODE[language]}|>"
                else:
                    if len(language) == 2:
                        # ISO 639-1 language code
                        acceptable_languages = list(TO_LANGUAGE_CODE.values())
                    elif "<" in language or "|" in language or ">" in language:
                        # generation config language code
                        acceptable_languages = list(generation_config.lang_to_id.keys())
                    else:
                        # language passed as a string
                        acceptable_languages = list(TO_LANGUAGE_CODE.keys())
                    raise ValueError(
                        f"Unsupported language: {language}. Language should be one of:" f" {acceptable_languages}."
                    )
                forced_decoder_ids.append((1, generation_config.lang_to_id[language_token]))

            if task is not None:
                forced_decoder_ids.append((2, generation_config.task_to_id[task]))
            else:
                forced_decoder_ids.append((2, generation_config.task_to_id["transcribe"]))

        if not return_timestamps:
            if forced_decoder_ids and forced_decoder_ids[-1][0] != generation_config.no_timestamps_token_id:
                idx = forced_decoder_ids[-1][0] + 1 if forced_decoder_ids else 1
                forced_decoder_ids.append((idx, generation_config.no_timestamps_token_id))

        return forced_decoder_ids

    def chunk_iter_with_batch(self, inputs, chunk_len, stride_left, stride_right, batch_size):
        inputs_len = inputs.shape[0]
        step = chunk_len - stride_left - stride_right

        all_chunk_start_idx = np.arange(0, inputs_len, step)
        num_samples = len(all_chunk_start_idx)

        num_batches = math.ceil(num_samples / batch_size)
        batch_idx = np.array_split(np.arange(num_samples), num_batches)

        for idx in batch_idx:
            chunk_start_idx = all_chunk_start_idx[idx]
            chunk_end_idx = chunk_start_idx + chunk_len

            chunks = [inputs[chunk_start:chunk_end] for chunk_start, chunk_end in zip(chunk_start_idx, chunk_end_idx)]
            processed = self.feature_extractor(
                chunks, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="np"
            )

            _stride_left = np.where(chunk_start_idx == 0, 0, stride_left)
            is_last = np.where(stride_right > 0, chunk_end_idx > inputs_len, chunk_end_idx >= inputs_len)
            _stride_right = np.where(is_last, 0, stride_right)

            chunk_lens = [chunk.shape[0] for chunk in chunks]
            strides = [
                (chunk_l, _stride_l, _stride_r)
                for chunk_l, _stride_l, _stride_r in zip(chunk_lens, _stride_left, _stride_right)
            ]

            yield {"stride": strides, **processed}

    def preprocess_batch(self, inputs, chunk_length_s=30.0, stride_length_s=None, batch_size=None):
        if isinstance(inputs, np.ndarray):
            logger.warning(
                "Numpy array passed as input - no sampling rate checks will be performed."
                "It is strongly recommended to pass the input as a dictionary with an 'array' key "
                "containing the numpy array representing the audio, and a 'sampling_rate' key "
                "containing the sampling rate associated with the audio array."
                "Failing to do so can result in silent errors that might be hard to debug."
            )

        if isinstance(inputs, str):
            if inputs.startswith("http://") or inputs.startswith("https://"):
                # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                # like http_huggingface_co.png
                inputs = requests.get(inputs).content
            else:
                with open(inputs, "rb") as f:
                    inputs = f.read()

        if isinstance(inputs, bytes):
            inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)

        stride = None
        if isinstance(inputs, dict):
            stride = inputs.get("stride", None)
            # Accepting `"array"` which is the key defined in `datasets` for
            # better integration
            if not ("sampling_rate" in inputs and "array" in inputs):
                raise ValueError(
                    "When passing a dictionary to FlaxWhisperPipline, the dict needs to contain an 'array' key "
                    "containing the numpy array representing the audio, and a 'sampling_rate' key "
                    "containing the sampling rate associated with the audio array."
                )

            in_sampling_rate = inputs.get("sampling_rate")
            inputs = inputs.get("array", None)

            if in_sampling_rate != self.feature_extractor.sampling_rate:
                try:
                    import librosa
                except ImportError as err:
                    raise ImportError(
                        "To support resampling audio files, please install 'librosa' and 'soundfile'."
                    ) from err

                inputs = librosa.resample(
                    inputs, orig_sr=in_sampling_rate, target_sr=self.feature_extractor.sampling_rate
                )
                ratio = self.feature_extractor.sampling_rate / in_sampling_rate
            else:
                ratio = 1

        if not isinstance(inputs, np.ndarray):
            raise ValueError(f"We expect a numpy ndarray as input, got `{type(inputs)}`")
        if len(inputs.shape) != 1:
            raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline")

        if stride is not None:
            if stride[0] + stride[1] > inputs.shape[0]:
                raise ValueError("Stride is too large for input")

            # Stride needs to get the chunk length here, it's going to get
            # swallowed by the `feature_extractor` later, and then batching
            # can add extra data in the inputs, so we need to keep track
            # of the original length in the stride so we can cut properly.
            stride = (inputs.shape[0], int(round(stride[0] * ratio)), int(round(stride[1] * ratio)))

        if chunk_length_s:
            if stride_length_s is None:
                stride_length_s = chunk_length_s / 6

            if isinstance(stride_length_s, (int, float)):
                stride_length_s = [stride_length_s, stride_length_s]

            chunk_len = round(chunk_length_s * self.feature_extractor.sampling_rate)
            stride_left = round(stride_length_s[0] * self.feature_extractor.sampling_rate)
            stride_right = round(stride_length_s[1] * self.feature_extractor.sampling_rate)

            if chunk_len < stride_left + stride_right:
                raise ValueError("Chunk length must be superior to stride length")

            for item in self.chunk_iter_with_batch(
                inputs,
                chunk_len,
                stride_left,
                stride_right,
                batch_size,
            ):
                yield item
        else:
            processed = self.feature_extractor(
                inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="np"
            )
            if stride is not None:
                processed["stride"] = stride
            yield processed

    def postprocess(self, model_outputs, return_timestamps=None, return_language=None):
        # unpack the outputs from list(dict(list)) to list(dict)
        model_outputs = [dict(zip(output, t)) for output in model_outputs for t in zip(*output.values())]

        time_precision = self.feature_extractor.chunk_length / self.model.config.max_source_positions
        # Send the chunking back to seconds, it's easier to handle in whisper
        sampling_rate = self.feature_extractor.sampling_rate
        for output in model_outputs:
            if "stride" in output:
                chunk_len, stride_left, stride_right = output["stride"]
                # Go back in seconds
                chunk_len /= sampling_rate
                stride_left /= sampling_rate
                stride_right /= sampling_rate
                output["stride"] = chunk_len, stride_left, stride_right

        text, optional = self.tokenizer._decode_asr(
            model_outputs,
            return_timestamps=return_timestamps,
            return_language=return_language,
            time_precision=time_precision,
        )
        return {"text": text, **optional}

    def forward(self, model_inputs, batch_size=None, language=None, task=None, return_timestamps=False):
        # We need to keep track of some additional input arguments for post-processing so need to forward these on after running generation
        input_features = model_inputs.pop("input_features")
        input_batch_size = input_features.shape[0]

        if input_batch_size != batch_size:
            padding = np.zeros([batch_size - input_batch_size, *input_features.shape[1:]], input_features.dtype)
            input_features = np.concatenate([input_features, padding])

        pred_ids = self.generate(input_features, language=language, task=task, return_timestamps=return_timestamps)[
            :input_batch_size
        ]

        # tokenizer's decode method expects an extra dim - we insert it here for convenience
        out = {"tokens": pred_ids[:, None, :]}

        stride = model_inputs.pop("stride", None)
        if stride is not None:
            out["stride"] = stride

        return out

    def __call__(
        self,
        inputs,
        chunk_length_s=30.0,
        stride_length_s=None,
        batch_size=None,
        language=None,
        task=None,
        return_timestamps=None,
        generate_kwargs=None,
    ):
        """
        Transcribe an audio input sequence to a text transcription, optionally with timestamps.

        Args:
            inputs (`np.ndarray` or `bytes` or `str` or `dict`):
                The inputs is either:
                    - `str` that is the filename of the audio file, the file will be read at the correct sampling rate
                      to get the waveform using *ffmpeg*. This requires *ffmpeg* to be installed on the system.
                    - `bytes` is the byte content of an audio file and is interpreted by *ffmpeg* in the
                      same way.
                    - (`np.ndarray` of shape (n, ) of type `np.float32` or `np.float64`)
                        Raw audio assumed to be at the correct sampling rate (16kHz). Note that no further sampling
                        rate check will be done.
                    - `dict` form can be used to pass raw audio sampled at arbitrary `sampling_rate` and let this
                      pipeline do the resampling. The dict must be in the format `{"sampling_rate": int, "array":
                      np.array}`. Optionally an additional argument `"stride": (left: int, right: int)` can be used to
                       ask the pipeline to treat the first `left` samples and last `right` samples to be ignored in
                       decoding (but used at inference to provide more context to the model). In general, this additional
                       stride argument is not required.
            chunk_length_s (`float`, *optional*, defaults to 30.0):
                The input length for each chunk. If `chunk_length_s = 0` then chunking is disabled. By default, the chunk
                length is set 30.0s, equal to Whisper's context window.
            stride_length_s (`float`, *optional*, defaults to `chunk_length_s / 6`):
                The length of stride on the left and right of each chunk. Used only with `chunk_length_s > 0`. This enables
                the model to *see* more context and infer letters better than without this context but the pipeline
                discards the stride bits at the end to make the final reconstitution as perfect as possible.

                <Tip>

                For more information on how to effectively use `stride_length_s`, refer to the [ASR chunking
                blog post](https://huggingface.co/blog/asr-chunking).

                </Tip>
            batch_size (`int`, *optional*, defaults to the minimum per-device batch size, i.e. `jax.local_device_count()`):
                The batch size to be used in chunking transcription. Beneficial for transcribing long audio files. Passing
                a batch size in the `__call__` method will supersede any batch size passed to the `__init__`.
            task (`str`, *optional*):
                Task to use for generation, either `"transcribe"` or `"translate"`. Defaults to `"transcribe"`.
            language (`str`, *optional*):
                Language token to use for generation, can be either in the form of `"<|en|>"`, `"en"` or `"english"`.
                Defaults to `None`, meaning the language is automatically inferred from the audio input.
            return_timestamps (*optional*, `bool`):
                Whether to return timestamps in the prediction. Defaults to False. If set to true, the pipeline
                will return two keys in the output dictionary: `"text"` containing the text transcription, and `"chunks"`
                containing the transcription segments chunked by their utterance-level timestamps.

        Return:
            `Dict`: A dictionary with the following keys:
                - **text** (`str` ) -- The recognised text.
                - **chunks** (*optional(, `List[Dict]`)
                    When using `return_timestamps`, the `chunks` will become a list containing all the various text
                    chunks identified by the model, *e.g.* `[{"text": "hi ", "timestamps": (0.5,0.9), {"text":
                    "there", "timestamps": (1.0, 1.5)}]`. The original full text can roughly be recovered by doing
                    `"".join(chunk["text"] for chunk in output["chunks"])`.
        """
        batch_size = batch_size if batch_size is not None else self.batch_size
        if batch_size % self.min_batch_size != 0:
            raise ValueError(
                f"Batch size must be a multiple of the number of JAX devices, but got batch size {batch_size} and num devices {self.min_batch_size}."
            )

        dataloader = self.preprocess_batch(
            inputs, chunk_length_s=chunk_length_s, stride_length_s=stride_length_s, batch_size=batch_size
        )
        model_outputs = []
        # iterate over our chunked audio samples
        for batch in dataloader:
            model_outputs.append(
                self.forward(
                    batch, batch_size=batch_size, language=language, task=task, return_timestamps=return_timestamps
                )
            )
        post_processed = self.postprocess(model_outputs, return_timestamps=return_timestamps)
        return post_processed