app.py

import logging
import os
from pathlib import Path

import speechbrain as sb
import torch
import torchaudio
from hyperpyyaml import load_hyperpyyaml
from speechbrain.utils.distributed import run_on_main

logger = logging.getLogger(__name__)


# Define training procedure
class ASR(sb.core.Brain):
    def compute_forward(self, batch, stage):
        """Forward computations from the waveform batches to the output probabilities."""

        batch = batch.to(self.device)
        wavs, wav_lens = batch.sig
        wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
        if stage == sb.Stage.TRAIN:
            if hasattr(self.hparams, "augmentation"):
                wavs = self.hparams.augmentation(wavs, wav_lens)

        # Forward pass
        feats = self.modules.wav2vec2(wavs, wav_lens)
        x = self.modules.enc(feats)
        logits = self.modules.ctc_lin(x)
        p_ctc = self.hparams.log_softmax(logits)

        return p_ctc, wav_lens

    def treat_wav(self, sig):
        feats = self.modules.wav2vec2(sig.to("cpu"), torch.tensor([1]).to("cpu"))
        feats = self.modules.enc(feats)
        logits = self.modules.ctc_lin(feats)
        p_ctc = self.hparams.log_softmax(logits)
        predicted_words = []
        for logs in p_ctc:
            text = decoder.decode(logs.detach().cpu().numpy())
            predicted_words.append(text.split(" "))
        return " ".join(predicted_words[0])

    def compute_objectives(self, predictions, batch, stage):
        """Computes the loss (CTC) given predictions and targets."""

        p_ctc, wav_lens = predictions

        ids = batch.id
        tokens, tokens_lens = batch.tokens

        loss = self.hparams.ctc_cost(p_ctc, tokens, wav_lens, tokens_lens)

        if stage != sb.Stage.TRAIN:
            predicted_tokens = sb.decoders.ctc_greedy_decode(
                p_ctc, wav_lens, blank_id=self.hparams.blank_index
            )
            # Decode token terms to words
            if self.hparams.use_language_modelling:
                predicted_words = []
                for logs in p_ctc:
                    text = decoder.decode(logs.detach().cpu().numpy())
                    predicted_words.append(text.split(" "))
            else:
                predicted_words = [
                    "".join(self.tokenizer.decode_ndim(utt_seq)).split(" ")
                    for utt_seq in predicted_tokens
                ]
            # Convert indices to words
            target_words = [wrd.split(" ") for wrd in batch.wrd]

            self.wer_metric.append(ids, predicted_words, target_words)
            self.cer_metric.append(ids, predicted_words, target_words)

        return loss

    def fit_batch(self, batch):
        """Train the parameters given a single batch in input"""
        should_step = self.step % self.grad_accumulation_factor == 0
        # Managing automatic mixed precision
        # TOFIX: CTC fine-tuning currently is unstable
        # This is certainly due to CTC being done in fp16 instead of fp32
        if self.auto_mix_prec:
            with torch.cuda.amp.autocast():
                with self.no_sync():
                    outputs = self.compute_forward(batch, sb.Stage.TRAIN)
                loss = self.compute_objectives(outputs, batch, sb.Stage.TRAIN)
            with self.no_sync(not should_step):
                self.scaler.scale(
                    loss / self.grad_accumulation_factor
                ).backward()
            if should_step:

                if not self.hparams.wav2vec2.freeze:
                    self.scaler.unscale_(self.wav2vec_optimizer)
                self.scaler.unscale_(self.model_optimizer)
                if self.check_gradients(loss):
                    if not self.hparams.wav2vec2.freeze:
                        if self.optimizer_step >= self.hparams.warmup_steps:
                            self.scaler.step(self.wav2vec_optimizer)
                    self.scaler.step(self.model_optimizer)
                self.scaler.update()
                self.zero_grad()
                self.optimizer_step += 1
        else:
            # This is mandatory because HF models have a weird behavior with DDP
            # on the forward pass
            with self.no_sync():
                outputs = self.compute_forward(batch, sb.Stage.TRAIN)

            loss = self.compute_objectives(outputs, batch, sb.Stage.TRAIN)

            with self.no_sync(not should_step):
                (loss / self.grad_accumulation_factor).backward()
            if should_step:
                if self.check_gradients(loss):
                    if not self.hparams.wav2vec2.freeze:
                        if self.optimizer_step >= self.hparams.warmup_steps:
                            self.wav2vec_optimizer.step()
                    self.model_optimizer.step()
                self.zero_grad()
                self.optimizer_step += 1

        self.on_fit_batch_end(batch, outputs, loss, should_step)
        return loss.detach().cpu()

    def evaluate_batch(self, batch, stage):
        """Computations needed for validation/test batches"""
        predictions = self.compute_forward(batch, stage=stage)
        with torch.no_grad():
            loss = self.compute_objectives(predictions, batch, stage=stage)
        return loss.detach()

    def on_stage_start(self, stage, epoch):
        """Gets called at the beginning of each epoch"""
        if stage != sb.Stage.TRAIN:
            self.cer_metric = self.hparams.cer_computer()
            self.wer_metric = self.hparams.error_rate_computer()

    def on_stage_end(self, stage, stage_loss, epoch):
        """Gets called at the end of an epoch."""
        # Compute/store important stats
        stage_stats = {"loss": stage_loss}
        if stage == sb.Stage.TRAIN:
            self.train_stats = stage_stats
        else:
            stage_stats["CER"] = self.cer_metric.summarize("error_rate")
            stage_stats["WER"] = self.wer_metric.summarize("error_rate")

        # Perform end-of-iteration things, like annealing, logging, etc.
        if stage == sb.Stage.VALID:
            old_lr_model, new_lr_model = self.hparams.lr_annealing_model(
                stage_stats["loss"]
            )
            old_lr_wav2vec, new_lr_wav2vec = self.hparams.lr_annealing_wav2vec(
                stage_stats["loss"]
            )
            sb.nnet.schedulers.update_learning_rate(
                self.model_optimizer, new_lr_model
            )
            if not self.hparams.wav2vec2.freeze:
                sb.nnet.schedulers.update_learning_rate(
                    self.wav2vec_optimizer, new_lr_wav2vec
                )
            self.hparams.train_logger.log_stats(
                stats_meta={
                    "epoch": epoch,
                    "lr_model": old_lr_model,
                    "lr_wav2vec": old_lr_wav2vec,
                },
                train_stats=self.train_stats,
                valid_stats=stage_stats,
            )
            self.checkpointer.save_and_keep_only(
                meta={"WER": stage_stats["WER"]}, min_keys=["WER"],
            )
        elif stage == sb.Stage.TEST:
            self.hparams.train_logger.log_stats(
                stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
                test_stats=stage_stats,
            )
            with open(self.hparams.wer_file, "w") as w:
                self.wer_metric.write_stats(w)

    def init_optimizers(self):
        "Initializes the wav2vec2 optimizer and model optimizer"

        # If the wav2vec encoder is unfrozen, we create the optimizer
        if not self.hparams.wav2vec2.freeze:
            self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
                self.modules.wav2vec2.parameters()
            )
            if self.checkpointer is not None:
                self.checkpointer.add_recoverable(
                    "wav2vec_opt", self.wav2vec_optimizer
                )

        self.model_optimizer = self.hparams.model_opt_class(
            self.hparams.model.parameters()
        )

        if self.checkpointer is not None:
            self.checkpointer.add_recoverable("modelopt", self.model_optimizer)

    def zero_grad(self, set_to_none=False):
        if not self.hparams.wav2vec2.freeze:
            self.wav2vec_optimizer.zero_grad(set_to_none)
        self.model_optimizer.zero_grad(set_to_none)


# Define custom data procedure
def dataio_prepare(hparams):
    """This function prepares the datasets to be used in the brain class.
    It also defines the data processing pipeline through user-defined functions."""

    # 1. Define datasets
    data_folder = hparams["data_folder"]

    train_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
        csv_path=hparams["train_csv"], replacements={"data_root": data_folder},
    )

    if hparams["sorting"] == "ascending":
        # we sort training data to speed up training and get better results.
        train_data = train_data.filtered_sorted(
            sort_key="duration",
            key_max_value={"duration": hparams["avoid_if_longer_than"]},
        )
        # when sorting do not shuffle in dataloader ! otherwise is pointless
        hparams["dataloader_options"]["shuffle"] = False

    elif hparams["sorting"] == "descending":
        train_data = train_data.filtered_sorted(
            sort_key="duration",
            reverse=True,
            key_max_value={"duration": hparams["avoid_if_longer_than"]},
        )
        # when sorting do not shuffle in dataloader ! otherwise is pointless
        hparams["dataloader_options"]["shuffle"] = False

    elif hparams["sorting"] == "random":
        pass

    else:
        raise NotImplementedError(
            "sorting must be random, ascending or descending"
        )

    valid_data = sb.dataio.dataset.DynamicItemDataset.from_csv(
        csv_path=hparams["valid_csv"], replacements={"data_root": data_folder},
    )
    # We also sort the validation data so it is faster to validate
    valid_data = valid_data.filtered_sorted(sort_key="duration")
    test_datasets = {}
    for csv_file in hparams["test_csv"]:
        name = Path(csv_file).stem
        test_datasets[name] = sb.dataio.dataset.DynamicItemDataset.from_csv(
            csv_path=csv_file, replacements={"data_root": data_folder}
        )
        test_datasets[name] = test_datasets[name].filtered_sorted(
            sort_key="duration"
        )

    datasets = [train_data, valid_data] + [i for k, i in test_datasets.items()]

    # 2. Define audio pipeline:
    @sb.utils.data_pipeline.takes("wav")
    @sb.utils.data_pipeline.provides("sig")
    def audio_pipeline(wav):
        info = torchaudio.info(wav)
        sig = sb.dataio.dataio.read_audio(wav)
        resampled = torchaudio.transforms.Resample(
            info.sample_rate, hparams["sample_rate"],
        )(sig)
        return resampled

    sb.dataio.dataset.add_dynamic_item(datasets, audio_pipeline)
    label_encoder = sb.dataio.encoder.CTCTextEncoder()

    # 3. Define text pipeline:
    @sb.utils.data_pipeline.takes("wrd")
    @sb.utils.data_pipeline.provides(
        "wrd", "char_list", "tokens_list", "tokens"
    )
    def text_pipeline(wrd):
        yield wrd
        char_list = list(wrd)
        yield char_list
        tokens_list = label_encoder.encode_sequence(char_list)
        yield tokens_list
        tokens = torch.LongTensor(tokens_list)
        yield tokens

    sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
    lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
    special_labels = {
        "blank_label": hparams["blank_index"],
        "unk_label": hparams["unk_index"]
    }
    label_encoder.load_or_create(
        path=lab_enc_file,
        from_didatasets=[train_data],
        output_key="char_list",
        special_labels=special_labels,
        sequence_input=True,
    )

    # 4. Set output:
    sb.dataio.dataset.set_output_keys(
        datasets, ["id", "sig", "wrd", "char_list", "tokens"],
    )
    return train_data, valid_data, test_datasets, label_encoder


# Load hyperparameters file with command-line overrides
hparams_file, run_opts, overrides = sb.parse_arguments(["train_semi.yaml"])
with open(hparams_file) as fin:
    hparams = load_hyperpyyaml(fin, overrides)

# If --distributed_launch then
# create ddp_group with the right communication protocol
sb.utils.distributed.ddp_init_group(run_opts)

# Create experiment directory
sb.create_experiment_directory(
    experiment_directory=hparams["output_folder"],
    hyperparams_to_save=hparams_file,
    overrides=overrides,
)

# Due to DDP, we do the preparation ONLY on the main python process
# Defining tokenizer and loading it
# Create the datasets objects as well as tokenization and encoding :-D
label_encoder = sb.dataio.encoder.CTCTextEncoder()

lab_enc_file = os.path.join(hparams["save_folder"], "label_encoder.txt")
special_labels = {
    "blank_label": hparams["blank_index"],
    "unk_label": hparams["unk_index"]
}
label_encoder.load_or_create(
    path=lab_enc_file,
    from_didatasets=[[]],
    output_key="char_list",
    special_labels=special_labels,
    sequence_input=True,
)

from pyctcdecode import build_ctcdecoder

ind2lab = label_encoder.ind2lab
labels = [ind2lab[x] for x in range(len(ind2lab))]
labels = [""] + labels[1:-1] + ["1"]
# Replace the <blank> token with a blank character, needed for PyCTCdecode

decoder = build_ctcdecoder(
    labels,
    kenlm_model_path=hparams["ngram_lm_path"],  # .arpa or .bin

    alpha=0.5,  # Default by KenLM
    beta=1.0,  # Default by KenLM
)
# Trainer initialization
run_opts["device"] = "cpu"
asr_brain = ASR(
    modules=hparams["modules"],
    hparams=hparams,
    run_opts=run_opts,
    checkpointer=hparams["checkpointer"],
)

# Adding objects to trainer.
asr_brain.tokenizer = label_encoder
asr_brain.checkpointer.recover_if_possible(device="cpu")
asr_brain.modules.eval()

title = "Tunisian Speech Recognition"

import os


def treat_wav_file(file_mic,file_upload ,asr=asr_brain, device="cpu") :
    if (file_mic is not None) and (file_upload is not None):
        warn_output = "WARNING: You've uploaded an audio file and used the microphone. The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
        wav = file_mic
    elif (file_mic is None) and (file_upload is None):
        return "ERROR: You have to either use the microphone or upload an audio file"
    elif file_mic is not None:
        wav = file_mic
    else:
        wav = file_upload
    info = torchaudio.info(wav)
    sr = info.sample_rate
    sig = sb.dataio.dataio.read_audio(wav)
    if len(sig.shape)>1 :
        sig = torch.mean(sig, dim=1)
    sig = torch.unsqueeze(sig, 0)
    tensor_wav = sig.to(device)
    resampled = torchaudio.functional.resample( tensor_wav, sr, 16000)
    sentence = asr.treat_wav(resampled)
    return sentence


#transcription = treat_wav_file(file_mic=None, file_upload="PTT-20240422-WA0007.flac")

# Print the transcription
#print("Transcription:", transcription)


import gradio as gr


gr.Interface(
    title = title,
    fn=treat_wav_file,
    inputs=[gr.Audio(sources="microphone", type='filepath', label = "record"),
            gr.Audio(sources="upload", type='filepath', label="filein")]
    ,outputs="text").launch(share=True)