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https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
update (#1841)
* v1.0.28 * version checker * version checker * rollback cif_v1 for training bug * fixbug * fixbug for cif * fixbug --------- Co-authored-by: 维石 <shixian.shi@alibaba-inc.com>
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zhifu gao
GitHub
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068a4054ef
@ -54,7 +54,7 @@ DISTRIBUTED_ARGS="
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--nproc_per_node $gpu_num \
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--node_rank ${RANK:-0} \
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--master_addr ${MASTER_ADDR:-127.0.0.1} \
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--master_port ${MASTER_PORT: 26669}
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--master_port ${MASTER_PORT:-26669}
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"
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echo $DISTRIBUTED_ARGS
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@ -55,7 +55,7 @@ DISTRIBUTED_ARGS="
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--nproc_per_node $gpu_num \
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--node_rank ${RANK:-0} \
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--master_addr ${MASTER_ADDR:-127.0.0.1} \
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--master_port ${MASTER_PORT: 26669}
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--master_port ${MASTER_PORT:-26669}
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"
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echo $DISTRIBUTED_ARGS
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@ -37,7 +37,7 @@ DISTRIBUTED_ARGS="
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--nproc_per_node $gpu_num \
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--node_rank ${RANK:-0} \
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--master_addr ${MASTER_ADDR:-127.0.0.1} \
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--master_port ${MASTER_PORT: 26669}
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--master_port ${MASTER_PORT:-26669}
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"
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echo $DISTRIBUTED_ARGS
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@ -37,7 +37,7 @@ DISTRIBUTED_ARGS="
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--nproc_per_node $gpu_num \
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--node_rank ${RANK:-0} \
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--master_addr ${MASTER_ADDR:-127.0.0.1} \
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--master_port ${MASTER_PORT: 26669}
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--master_port ${MASTER_PORT:-26669}
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"
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echo $DISTRIBUTED_ARGS
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@ -51,7 +51,7 @@ DISTRIBUTED_ARGS="
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--nproc_per_node $gpu_num \
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--node_rank ${RANK:-0} \
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--master_addr ${MASTER_ADDR:-127.0.0.1} \
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--master_port ${MASTER_PORT: 26669}
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--master_port ${MASTER_PORT:-26669}
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"
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echo $DISTRIBUTED_ARGS
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@ -52,7 +52,7 @@ DISTRIBUTED_ARGS="
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--nproc_per_node $gpu_num \
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--node_rank ${RANK:-0} \
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--master_addr ${MASTER_ADDR:-127.0.0.1} \
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--master_port ${MASTER_PORT: 26669}
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--master_port ${MASTER_PORT:-26669}
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"
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echo $DISTRIBUTED_ARGS
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@ -50,7 +50,7 @@ DISTRIBUTED_ARGS="
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--nproc_per_node $gpu_num \
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--node_rank ${RANK:-0} \
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--master_addr ${MASTER_ADDR:-127.0.0.1} \
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--master_port ${MASTER_PORT: 26669}
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--master_port ${MASTER_PORT:-26669}
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"
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echo $DISTRIBUTED_ARGS
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@ -52,7 +52,7 @@ DISTRIBUTED_ARGS="
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--nproc_per_node $gpu_num \
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--node_rank ${RANK:-0} \
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--master_addr ${MASTER_ADDR:-127.0.0.1} \
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--master_port ${MASTER_PORT: 26669}
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--master_port ${MASTER_PORT:-26669}
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"
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echo $DISTRIBUTED_ARGS
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@ -111,6 +111,13 @@ class AutoModel:
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def __init__(self, **kwargs):
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try:
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from funasr.utils.version_checker import check_for_update
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check_for_update()
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except:
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pass
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log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
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logging.basicConfig(level=log_level)
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@ -33,8 +33,8 @@ def compute_wer(
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if cn_postprocess:
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value = " ".join(value)
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value = value.replace(" ", "")
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if value[0] == "请":
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value = value[1:]
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# if value[0] == "请":
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# value = value[1:]
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value = [x for x in value]
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hyp_dict[key] = value
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with open(ref_file, "r") as ref_reader:
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@ -80,7 +80,7 @@ class CifPredictor(torch.nn.Module):
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hidden, alphas, token_num, mask=mask
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)
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acoustic_embeds, cif_peak = cif_v1(hidden, alphas, self.threshold)
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acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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if target_length is None and self.tail_threshold > 0.0:
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token_num_int = torch.max(token_num).type(torch.int32).item()
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@ -245,7 +245,7 @@ class CifPredictorV2(torch.nn.Module):
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hidden, alphas, token_num, mask=None
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)
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acoustic_embeds, cif_peak = cif_v1(hidden, alphas, self.threshold)
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acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
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if target_length is None and self.tail_threshold > 0.0:
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token_num_int = torch.max(token_num).type(torch.int32).item()
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acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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@ -506,7 +506,10 @@ def cif_v1_export(hidden, alphas, threshold: float):
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frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
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fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)
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prefix_sum = torch.cumsum(alphas, dim=1)
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# prefix_sum = torch.cumsum(alphas, dim=1)
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prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(
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torch.float32
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) # cumsum precision degradation cause wrong result in extreme
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prefix_sum_floor = torch.floor(prefix_sum)
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dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)
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dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)
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@ -518,8 +521,8 @@ def cif_v1_export(hidden, alphas, threshold: float):
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fires[fire_idxs] = 1
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fires = fires + prefix_sum - prefix_sum_floor
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# prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
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prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
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frames = prefix_sum_hidden[fire_idxs]
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shift_frames = torch.roll(frames, 1, dims=0)
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@ -530,6 +533,7 @@ def cif_v1_export(hidden, alphas, threshold: float):
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shift_frames[shift_batch_idxs] = 0
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remains = fires - torch.floor(fires)
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# remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
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remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
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shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
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@ -537,8 +541,11 @@ def cif_v1_export(hidden, alphas, threshold: float):
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frames = frames - shift_frames + shift_remain_frames - remain_frames
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max_label_len = batch_len.max()
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# max_label_len = batch_len.max()
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max_label_len = alphas.sum(dim=-1)
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max_label_len = torch.floor(max_label_len).max().to(dtype=torch.int64)
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# frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
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frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
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indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)
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frame_fires_idxs = indices < batch_len.unsqueeze(1)
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@ -667,7 +674,10 @@ def cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=False):
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fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)
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prefix_sum = torch.cumsum(alphas, dim=1)
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# prefix_sum = torch.cumsum(alphas, dim=1)
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prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(
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torch.float32
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) # cumsum precision degradation cause wrong result in extreme
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prefix_sum_floor = torch.floor(prefix_sum)
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dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)
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dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)
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@ -689,6 +699,8 @@ def cif_v1(hidden, alphas, threshold):
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device = hidden.device
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dtype = hidden.dtype
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batch_size, len_time, hidden_size = hidden.size()
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# frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
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# prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
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frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
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prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
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@ -702,6 +714,7 @@ def cif_v1(hidden, alphas, threshold):
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shift_frames[shift_batch_idxs] = 0
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remains = fires - torch.floor(fires)
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# remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
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remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
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shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
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@ -709,8 +722,12 @@ def cif_v1(hidden, alphas, threshold):
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frames = frames - shift_frames + shift_remain_frames - remain_frames
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max_label_len = batch_len.max()
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# max_label_len = batch_len.max()
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max_label_len = (
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torch.round(alphas.sum(-1)).int().max()
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) # torch.round to calculate the max length
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# frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
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frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
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indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)
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frame_fires_idxs = indices < batch_len.unsqueeze(1)
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24
funasr/utils/version_checker.py
Normal file
24
funasr/utils/version_checker.py
Normal file
@ -0,0 +1,24 @@
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import requests
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from packaging import version
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from funasr import __version__ # Ensure that __version__ is defined in your package's __init__.py
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def get_pypi_version(package_name):
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url = f"https://pypi.org/pypi/{package_name}/json"
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response = requests.get(url)
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if response.status_code == 200:
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data = response.json()
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return version.parse(data["info"]["version"])
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else:
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raise Exception("Failed to retrieve version information from PyPI.")
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def check_for_update():
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current_version = version.parse(__version__)
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pypi_version = get_pypi_version("funasr")
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if current_version < pypi_version:
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print(f"New version available: {pypi_version}. Your current version is {current_version}.")
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print('Please use the command "pip install -U funasr" to upgrade.')
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else:
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print(f"You are using the latest version of funasr-{current_version}")
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