mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-09-07 08:04:18 +00:00
add pretrained to pruned_transducer_stateless2
This commit is contained in:
Gaurav Shegokar
parent
cc678861d4
commit
7b2239352c
@ -210,8 +210,7 @@ def get_parser():
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default="output",
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help="""
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Output directory name, we store output hypothesis
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"""
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""",
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)
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parser.add_argument(
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@ -220,7 +219,7 @@ def get_parser():
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default=10,
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help="""
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Number of input files in one batch, defaulted to 10
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"""
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""",
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)
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parser.add_argument(
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"sound_files",
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@ -302,7 +301,7 @@ def decode_one_batch(
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feature: torch.Tensor,
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bpe_model: Optional[spm.SentencePieceProcessor] = None,
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G: Optional[k2.Fsa] = None,
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) -> Dict[str, List[List[str]]]:
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) -> List[List[str]]:
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device = decoding_graph.device
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assert feature.ndim == 3
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@ -404,11 +403,14 @@ def main():
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params.update(vars(args))
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su = ShortUUID(alphabet=string.ascii_lowercase + string.digits)
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output_dir = Path(args.output_dir)/(su.random(length=5) + '-' + datetime.datetime.now().strftime(
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"%Y-%m-%d_%H-%M"))
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output_dir = Path(args.output_dir) / (
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su.random(length=5)
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+ "-"
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+ datetime.datetime.now().strftime("%Y-%m-%d_%H-%M")
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)
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output_dir.mkdir(exist_ok=True, parents=True)
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with (output_dir/"command").open("w") as f_cmd:
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with (output_dir / "command").open("w") as f_cmd:
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f_cmd.write(f"{args}\n")
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f_cmd.write(f"{params}\n")
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logging.info(f"{params}")
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@ -502,23 +504,29 @@ def main():
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else:
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raise ValueError(f"Unsupported decoding method: {params.method}")
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testdata = TestDataset(features)
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tl = DataLoader(testdata, batch_size=args.batch_size)
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inputdata = TestDataset(features)
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tl = DataLoader(inputdata, batch_size=args.batch_size)
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hyps = []
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num_batches = len(tl)
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for batch_idx, batch in enumerate(tl):
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hyps.extend(decode_one_batch(
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params, model, decoding_graph, batch[0], bpe_model, G))
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hyps.extend(
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decode_one_batch(
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params, model, decoding_graph, batch[0], bpe_model, G
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)
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)
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logging.info(
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f"batch {batch_idx + 1}/{num_batches}, cuts processed until now is {len(hyps)}")
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f"batch {batch_idx + 1}/{num_batches}, cuts processed until now is {len(hyps)}"
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)
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logging.info(f"Writing hypothesis to output dir {output_dir}")
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s = "\n"
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for filename, hyp in zip(wave_names, hyps):
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words = " ".join(hyp)
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s += f"{filename}:\n{words}\n\n"
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with (output_dir/os.path.basename(filename.replace(".wav", ".txt"))).open("w") as f_hyp:
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with (
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output_dir / os.path.basename(filename.replace(".wav", ".txt"))
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).open("w") as f_hyp:
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f_hyp.write(words)
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logging.info(s)
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logging.info("Decoding Done")
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@ -59,9 +59,15 @@ def get_parser():
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"--num-splits",
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type=int,
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required=True,
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help="The number of splits of the XL subset",
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help="The number of splits of the subset",
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)
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parser.add_argument(
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"--subset",
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type=str,
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default="XL",
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help="subset name, XL, L, S, XS"
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)
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parser.add_argument(
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"--start",
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type=int,
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@ -80,7 +86,7 @@ def get_parser():
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def compute_fbank_gigaspeech_splits(args):
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num_splits = args.num_splits
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output_dir = "data/fbank/XL_split"
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output_dir = f"data/fbank/{args.subset}_split"
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output_dir = Path(output_dir)
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assert output_dir.exists(), f"{output_dir} does not exist!"
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@ -103,12 +109,12 @@ def compute_fbank_gigaspeech_splits(args):
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idx = f"{i + 1}".zfill(num_digits)
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logging.info(f"Processing {idx}/{num_splits}")
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cuts_path = output_dir / f"cuts_XL.{idx}.jsonl.gz"
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cuts_path = output_dir / f"cuts_{args.subset}.{idx}.jsonl.gz"
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if cuts_path.is_file():
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logging.info(f"{cuts_path} exists - skipping")
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continue
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raw_cuts_path = output_dir / f"cuts_XL_raw.{idx}.jsonl.gz"
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raw_cuts_path = output_dir / f"cuts_{args.subset}_raw.{idx}.jsonl.gz"
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logging.info(f"Loading {raw_cuts_path}")
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cut_set = CutSet.from_file(raw_cuts_path)
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@ -117,7 +123,7 @@ def compute_fbank_gigaspeech_splits(args):
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cut_set = cut_set.compute_and_store_features_batch(
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extractor=extractor,
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storage_path=f"{output_dir}/feats_XL_{idx}",
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storage_path=f"{output_dir}/feats_{args.subset}_{idx}",
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num_workers=args.num_workers,
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batch_duration=args.batch_duration,
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)
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@ -47,11 +47,7 @@ def preprocess_giga_speech():
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output_dir = Path("data/fbank")
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output_dir.mkdir(exist_ok=True)
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dataset_parts = (
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"DEV",
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"TEST",
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"XL",
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)
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dataset_parts = "S"
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logging.info("Loading manifest (may take 4 minutes)")
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manifests = read_manifests_if_cached(
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@ -86,16 +82,16 @@ def preprocess_giga_speech():
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)
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# Run data augmentation that needs to be done in the
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# time domain.
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if partition not in ["DEV", "TEST"]:
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logging.info(
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f"Speed perturb for {partition} with factors 0.9 and 1.1 "
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"(Perturbing may take 8 minutes and saving may take 20 minutes)"
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)
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cut_set = (
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cut_set
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+ cut_set.perturb_speed(0.9)
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+ cut_set.perturb_speed(1.1)
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)
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# if partition not in ["DEV", "TEST"]:
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# logging.info(
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# f"Speed perturb for {partition} with factors 0.9 and 1.1 "
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# "(Perturbing may take 8 minutes and saving may take 20 minutes)"
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# )
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# cut_set = (
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# cut_set
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# + cut_set.perturb_speed(0.9)
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# + cut_set.perturb_speed(1.1)
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# )
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logging.info(f"Saving to {raw_cuts_path}")
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cut_set.to_file(raw_cuts_path)
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@ -503,34 +503,37 @@ def main():
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logging.info("About to create model")
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model = get_transducer_model(params)
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if params.iter > 0:
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filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
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: params.avg
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]
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if len(filenames) == 0:
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raise ValueError(
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f"No checkpoints found for"
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f" --iter {params.iter}, --avg {params.avg}"
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)
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elif len(filenames) < params.avg:
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raise ValueError(
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f"Not enough checkpoints ({len(filenames)}) found for"
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f" --iter {params.iter}, --avg {params.avg}"
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)
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logging.info(f"averaging {filenames}")
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model.to(device)
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model.load_state_dict(average_checkpoints(filenames, device=device))
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elif params.avg == 1:
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load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
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else:
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start = params.epoch - params.avg + 1
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filenames = []
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for i in range(start, params.epoch + 1):
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if start >= 0:
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filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
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logging.info(f"averaging {filenames}")
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model.to(device)
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model.load_state_dict(average_checkpoints(filenames, device=device))
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load_checkpoint(f"{params.exp_dir}/pretrained-iter-3488000-avg-20.pt", model)
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# we don't average on models
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# if params.iter > 0:
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# filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
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# : params.avg
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# ]
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# if len(filenames) == 0:
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# raise ValueError(
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# f"No checkpoints found for"
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# f" --iter {params.iter}, --avg {params.avg}"
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# )
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# elif len(filenames) < params.avg:
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# raise ValueError(
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# f"Not enough checkpoints ({len(filenames)}) found for"
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# f" --iter {params.iter}, --avg {params.avg}"
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# )
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# logging.info(f"averaging {filenames}")
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# model.to(device)
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# model.load_state_dict(average_checkpoints(filenames, device=device))
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# elif params.avg == 1:
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# load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
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# else:
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# start = params.epoch - params.avg + 1
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# filenames = []
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# for i in range(start, params.epoch + 1):
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# if start >= 0:
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# filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
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# logging.info(f"averaging {filenames}")
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# model.to(device)
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# model.load_state_dict(average_checkpoints(filenames, device=device))
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model.to(device)
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model.eval()
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440
egs/gigaspeech/ASR/pruned_transducer_stateless2/pretrained.py
Executable file
440
egs/gigaspeech/ASR/pruned_transducer_stateless2/pretrained.py
Executable file
@ -0,0 +1,440 @@
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#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Usage:
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(1) greedy search
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./pruned_transducer_stateless2/pretrained.py \
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--checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--method greedy_search \
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/path/to/foo.wav \
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/path/to/bar.wav
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(2) beam search
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./pruned_transducer_stateless2/pretrained.py \
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--checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--method beam_search \
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--beam-size 4 \
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/path/to/foo.wav \
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/path/to/bar.wav
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(3) modified beam search
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./pruned_transducer_stateless2/pretrained.py \
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--checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--method modified_beam_search \
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--beam-size 4 \
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/path/to/foo.wav \
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/path/to/bar.wav
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(4) fast beam search
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./pruned_transducer_stateless2/pretrained.py \
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--checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
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--bpe-model ./data/lang_bpe_500/bpe.model \
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--method fast_beam_search \
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--beam-size 4 \
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/path/to/foo.wav \
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/path/to/bar.wav
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You can also use `./pruned_transducer_stateless2/exp/epoch-xx.pt`.
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Note: ./pruned_transducer_stateless2/exp/pretrained.pt is generated by
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./pruned_transducer_stateless2/export.py
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"""
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import os
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import argparse
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import logging
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import math
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import string
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import datetime
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from pathlib import Path
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from typing import List
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from icefall.utils import setup_logger
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import k2
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import kaldifeat
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import sentencepiece as spm
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import torch
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import torch.nn as nn
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from torch.utils.data import Dataset, DataLoader
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import torchaudio
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from beam_search import (
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beam_search,
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fast_beam_search_one_best,
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greedy_search,
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greedy_search_batch,
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modified_beam_search,
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)
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from torch.nn.utils.rnn import pad_sequence
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from train import get_params, get_transducer_model
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from shortuuid import ShortUUID
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from icefall.utils import AttributeDict
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def get_parser():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter
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)
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parser.add_argument(
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"--checkpoint",
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type=str,
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required=True,
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help="Path to the checkpoint. "
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"The checkpoint is assumed to be saved by "
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"icefall.checkpoint.save_checkpoint().",
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)
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parser.add_argument(
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"--bpe-model",
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type=str,
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help="""Path to bpe.model.""",
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)
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parser.add_argument(
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"--method",
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type=str,
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default="greedy_search",
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help="""Possible values are:
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- greedy_search
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- beam_search
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- modified_beam_search
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- fast_beam_search
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""",
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)
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parser.add_argument(
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"sound_files",
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type=str,
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nargs="+",
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help="The input sound file(s) to transcribe. "
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"Supported formats are those supported by torchaudio.load(). "
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"For example, wav and flac are supported. "
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"The sample rate has to be 16kHz.",
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)
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parser.add_argument(
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"--sample-rate",
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type=int,
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default=16000,
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help="The sample rate of the input sound file",
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)
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parser.add_argument(
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"--beam-size",
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type=int,
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default=4,
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help="""An integer indicating how many candidates we will keep for each
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frame. Used only when --method is beam_search or
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modified_beam_search.""",
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)
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parser.add_argument(
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"--beam",
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type=float,
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default=4,
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help="""A floating point value to calculate the cutoff score during beam
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search (i.e., `cutoff = max-score - beam`), which is the same as the
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`beam` in Kaldi.
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Used only when --method is fast_beam_search""",
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)
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parser.add_argument(
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"--max-contexts",
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type=int,
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default=4,
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help="""Used only when --method is fast_beam_search""",
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)
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parser.add_argument(
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"--max-states",
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type=int,
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default=8,
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help="""Used only when --method is fast_beam_search""",
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)
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parser.add_argument(
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"--context-size",
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type=int,
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default=2,
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help="The context size in the decoder. 1 means bigram; "
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"2 means tri-gram",
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)
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parser.add_argument(
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"--max-sym-per-frame",
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type=int,
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default=1,
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help="""Maximum number of symbols per frame. Used only when
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--method is greedy_search.
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""",
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)
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parser.add_argument(
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"--output",
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type=str,
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default="birch/output",
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help="output directory name",
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)
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parser.add_argument(
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"--batch-size",
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type=int,
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default=10,
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)
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return parser
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def read_sound_files(
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filenames: List[str], expected_sample_rate: float
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) -> List[torch.Tensor]:
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"""Read a list of sound files into a list 1-D float32 torch tensors.
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Args:
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filenames:
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A list of sound filenames.
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expected_sample_rate:
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The expected sample rate of the sound files.
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Returns:
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Return a list of 1-D float32 torch tensors.
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"""
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ans = []
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wave_names = []
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def loadfile(filename):
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wave, sample_rate = torchaudio.load(filename)
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assert sample_rate == expected_sample_rate, (
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f"expected sample rate: {expected_sample_rate}. "
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f"Given: {sample_rate}"
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)
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# We use only the first channel
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ans.append(wave[0])
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wave_names.append(str(filename))
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|
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for f in filenames:
|
||||
file_path = Path(f)
|
||||
if file_path.is_file():
|
||||
loadfile(file_path)
|
||||
elif file_path.is_dir():
|
||||
for filename in file_path.iterdir():
|
||||
loadfile(filename)
|
||||
else:
|
||||
logging.error(f"{f} must be a filename or a dirname")
|
||||
return ans, wave_names
|
||||
|
||||
|
||||
def decode_one_batch(
|
||||
params: AttributeDict,
|
||||
model: nn.Module,
|
||||
features: torch.tensor,
|
||||
sp: spm.SentencePieceProcessor,
|
||||
) -> List[List[str]]:
|
||||
|
||||
device = features.device
|
||||
feature_lengths = [f.size(0) for f in features]
|
||||
feature_lengths = torch.tensor(feature_lengths, device=device)
|
||||
|
||||
encoder_out, encoder_out_lens = model.encoder(
|
||||
x=features, x_lens=feature_lengths
|
||||
)
|
||||
|
||||
num_waves = encoder_out.size(0)
|
||||
hyps = []
|
||||
msg = f"Using {params.method}"
|
||||
if params.method == "beam_search":
|
||||
msg += f" with beam size {params.beam_size}"
|
||||
logging.debug(msg)
|
||||
|
||||
if params.method == "fast_beam_search":
|
||||
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
|
||||
hyp_tokens = fast_beam_search_one_best(
|
||||
model=model,
|
||||
decoding_graph=decoding_graph,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
beam=params.beam,
|
||||
max_contexts=params.max_contexts,
|
||||
max_states=params.max_states,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(hyp.split())
|
||||
elif params.method == "modified_beam_search":
|
||||
hyp_tokens = modified_beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(hyp.split())
|
||||
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
|
||||
hyp_tokens = greedy_search_batch(
|
||||
model=model,
|
||||
encoder_out=encoder_out,
|
||||
encoder_out_lens=encoder_out_lens,
|
||||
)
|
||||
for hyp in sp.decode(hyp_tokens):
|
||||
hyps.append(hyp.split())
|
||||
else:
|
||||
for i in range(num_waves):
|
||||
# fmt: off
|
||||
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
|
||||
# fmt: on
|
||||
if params.method == "greedy_search":
|
||||
hyp = greedy_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
max_sym_per_frame=params.max_sym_per_frame,
|
||||
)
|
||||
elif params.method == "beam_search":
|
||||
hyp = beam_search(
|
||||
model=model,
|
||||
encoder_out=encoder_out_i,
|
||||
beam=params.beam_size,
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unsupported method: {params.method}")
|
||||
|
||||
hyps.append(sp.decode(hyp).split())
|
||||
return hyps
|
||||
|
||||
|
||||
class TestDataset(Dataset):
|
||||
def __init__(self, features: torch.Tensor):
|
||||
self.features = features
|
||||
|
||||
def __len__(self):
|
||||
return len(self.features)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return (self.features[idx], 0)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def main():
|
||||
parser = get_parser()
|
||||
args = parser.parse_args()
|
||||
|
||||
params = get_params()
|
||||
|
||||
params.update(vars(args))
|
||||
|
||||
sp = spm.SentencePieceProcessor()
|
||||
sp.load(params.bpe_model)
|
||||
|
||||
# <blk> is defined in local/train_bpe_model.py
|
||||
params.blank_id = sp.piece_to_id("<blk>")
|
||||
params.unk_id = sp.piece_to_id("<unk>")
|
||||
params.vocab_size = sp.get_piece_size()
|
||||
su = ShortUUID(alphabet=string.ascii_lowercase + string.digits)
|
||||
|
||||
params.suffix = f"-pruned-transducer-stateless2-{params.method}"
|
||||
if "fast_beam_search" in params.method:
|
||||
params.suffix += f"-beam-{params.beam}"
|
||||
params.suffix += f"-max-contexts-{params.max_contexts}"
|
||||
params.suffix += f"-max-states-{params.max_states}"
|
||||
elif "beam_search" in params.method:
|
||||
params.suffix += f"-beam-{params.beam_size}"
|
||||
else:
|
||||
params.suffix += f"-context-{params.context_size}"
|
||||
params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
|
||||
output_dir = Path(params.output) / (
|
||||
su.random(length=5)
|
||||
+ "-"
|
||||
+ datetime.datetime.now().strftime("%Y-%m-%d_%H-%M")
|
||||
+ params.suffix
|
||||
)
|
||||
output_dir.mkdir(exist_ok=True, parents=True)
|
||||
setup_logger(f"{output_dir}/log-decode")
|
||||
|
||||
logging.info(f"{params}")
|
||||
|
||||
device = torch.device("cpu")
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda", 0)
|
||||
|
||||
logging.info(f"device: {device}")
|
||||
|
||||
logging.info("Creating model")
|
||||
model = get_transducer_model(params)
|
||||
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
logging.info(f"Number of model parameters: {num_param}")
|
||||
|
||||
checkpoint = torch.load(params.checkpoint, map_location="cpu")
|
||||
model.load_state_dict(checkpoint["model"], strict=False)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
model.device = device
|
||||
|
||||
logging.info("Constructing Fbank computer")
|
||||
opts = kaldifeat.FbankOptions()
|
||||
opts.device = device
|
||||
opts.frame_opts.dither = 0
|
||||
opts.frame_opts.snip_edges = False
|
||||
opts.frame_opts.samp_freq = params.sample_rate
|
||||
opts.mel_opts.num_bins = params.feature_dim
|
||||
|
||||
fbank = kaldifeat.Fbank(opts)
|
||||
|
||||
logging.info(f"Reading sound files: {params.sound_files}")
|
||||
waves, wavnames = read_sound_files(
|
||||
filenames=params.sound_files, expected_sample_rate=params.sample_rate
|
||||
)
|
||||
waves = [w.to(device) for w in waves]
|
||||
|
||||
logging.info("Decoding started")
|
||||
features = fbank(waves)
|
||||
features = pad_sequence(
|
||||
features, batch_first=True, padding_value=math.log(1e-10)
|
||||
)
|
||||
inputdata = TestDataset(features)
|
||||
tl = DataLoader(inputdata, batch_size=params.batch_size)
|
||||
|
||||
num_batches = len(tl)
|
||||
hyps = []
|
||||
for batch_idx, batch in enumerate(tl):
|
||||
hyps.extend(decode_one_batch(params, model, batch[0], sp))
|
||||
logging.info(
|
||||
f"batch {batch_idx + 1}/{num_batches}, cuts processed until now is {len(hyps)}"
|
||||
)
|
||||
|
||||
s = "\n"
|
||||
assert len(wavnames) == len(hyps)
|
||||
for filename, hyp in zip(wavnames, hyps):
|
||||
words = " ".join(hyp)
|
||||
s += f"{filename}:\n{words}\n\n"
|
||||
with (
|
||||
output_dir / os.path.basename(filename.replace(".wav", ".txt"))
|
||||
).open("w") as fhyp:
|
||||
fhyp.write(words)
|
||||
|
||||
logging.info(s)
|
||||
|
||||
logging.info("Decoding Done")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
formatter = (
|
||||
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
|
||||
)
|
||||
|
||||
logging.basicConfig(format=formatter, level=logging.INFO)
|
||||
main()
|
||||
Loading…
x
Reference in New Issue
Block a user