def plotfn(self, data, attn_dict, outdir, suffix="png", savefn=None):
"""Plot multi head attentions.
Args:
data (dict): Utts info from json file.
attn_dict (dict): Multi head attention dict.
Values should be numpy.ndarray (H, L, T)
outdir (str): Directory name to save figures.
suffix (str): Filename suffix including image type (e.g., png).
savefn (function): Function to save figures.
"""
import matplotlib.pyplot as plt
for name, att_ws in attn_dict.items():
for idx, att_w in enumerate(att_ws):
filename = "%s/%s.%s.%s" % (outdir, data[idx][0], name, suffix)
if "fbank" in name:
fig = plt.Figure()
ax = fig.subplots(1, 1)
ax.imshow(att_w, aspect="auto")
ax.set_xlabel("frames")
ax.set_ylabel("fbank coeff")
fig.tight_layout()
else:
fig = _plot_and_save_attention(att_w, filename)
savefn(fig, filename)
def plotfn(
self, data_dict, uttid_list, attn_dict, outdir, suffix="png", savefn=None
):
"""Plot multi head attentions.
Args:
data_dict (dict): Utts info from json file.
uttid_list (list): List of utt_id.
attn_dict (dict): Multi head attention dict.
Values should be numpy.ndarray (H, L, T)
outdir (str): Directory name to save figures.
suffix (str): Filename suffix including image type (e.g., png).
savefn (function): Function to save figures.
"""
import matplotlib.pyplot as plt
from espnet.nets.pytorch_backend.transformer.plot import (
_plot_and_save_attention, # noqa: H301
)
for name, att_ws in attn_dict.items():
for utt_id, att_w in zip(uttid_list, att_ws):
filename = "%s/%s.%s.%s" % (outdir, utt_id, name, suffix)
if "fbank" in name:
fig = plt.Figure()
ax = fig.subplots(1, 1)
ax.imshow(att_w, aspect="auto")
ax.set_xlabel("frames")
ax.set_ylabel("fbank coeff")
fig.tight_layout()
else:
fig = _plot_and_save_attention(att_w, filename)
savefn(fig, filename)
def plotfn(self, data, attn_dict, outdir, suffix="png", savefn=None):
import matplotlib.pyplot as plt
for name, att_ws in attn_dict.items():
for idx, att_w in enumerate(att_ws):
filename = "%s/%s.%s.%s" % (outdir, data[idx][0], name, suffix)
if "fbank" in name:
fig = plt.Figure()
ax = fig.subplots(1, 1)
ax.imshow(att_w, aspect="auto")
ax.set_xlabel("frames")
ax.set_ylabel("fbank coeff")
fig.tight_layout()
else:
fig = _plot_and_save_attention(att_w, filename)
savefn(fig, filename)
def plotfn(self, data, attn_dict, outdir, suffix="png", savefn=None):
"""Plot multi head attentions
:param dict data: utts info from json file
:param dict[str, torch.Tensor] attn_dict: multi head attention dict.
values should be torch.Tensor (head, input_length, output_length)
:param str outdir: dir to save fig
:param str suffix: filename suffix including image type (e.g., png)
:param savefn: function to save
"""
import matplotlib.pyplot as plt
for name, att_ws in attn_dict.items():
for idx, att_w in enumerate(att_ws):
filename = "%s/%s.%s.%s" % (outdir, data[idx][0], name, suffix)
if "fbank" in name:
fig = plt.Figure()
ax = fig.subplots(1, 1)
ax.imshow(att_w, aspect="auto")
ax.set_xlabel("frames")
ax.set_ylabel("fbank coeff")
fig.tight_layout()
else:
fig = _plot_and_save_attention(att_w, filename)
savefn(fig, filename)
