We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Cut concatenate doesn't consider the first sample in each batch
Found in #234 

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `lhotse/dataset/cut_transforms/concatenate.py`
Content:
```
1 from typing import Optional, Sequence
2 
3 from lhotse import CutSet
4 from lhotse.cut import AnyCut
5 from lhotse.utils import Seconds
6 
7 
8 class CutConcatenate:
9     """
10     A transform on batch of cuts (``CutSet``) that concatenates the cuts to minimize the total amount of padding;
11     e.g. instead of creating a batch with 40 examples, we will merge some of the examples together
12     adding some silence between them to avoid a large number of padding frames that waste the computation.
13     """
14 
15     def __init__(
16             self,
17             gap: Seconds = 1.0,
18             duration_factor: float = 1.0
19     ) -> None:
20         """
21         CutConcatenate's constructor.
22 
23         :param gap: The duration of silence in seconds that is inserted between the cuts;
24             it's goal is to let the model "know" that there are separate utterances in a single example.
25         :param duration_factor: Determines the maximum duration of the concatenated cuts;
26             by default it's 1, setting the limit at the duration of the longest cut in the batch.
27         """
28         self.gap = gap
29         self.duration_factor = duration_factor
30 
31     def __call__(self, cuts: CutSet) -> CutSet:
32         cuts = cuts.sort_by_duration(ascending=False)
33         return concat_cuts(
34             cuts,
35             gap=self.gap,
36             max_duration=cuts[0].duration * self.duration_factor
37         )
38 
39 
40 def concat_cuts(
41         cuts: Sequence[AnyCut],
42         gap: Seconds = 1.0,
43         max_duration: Optional[Seconds] = None
44 ) -> CutSet:
45     """
46     We're going to concatenate the cuts to minimize the amount of total padding frames used.
47     This means that some samples in the batch will be merged together into one sample,
48     separated by an interval of silence.
49     This is actually solving a knapsack problem.
50     In this initial implementation we're using a greedy approach:
51     going from the back (i.e. the shortest cuts) we'll try to concat them to the longest cut
52     that still has some "space" at the end.
53 
54     :param cuts: a list of cuts to pack.
55     :param gap: the duration of silence inserted between concatenated cuts.
56     :param max_duration: the maximum duration for the concatenated cuts
57         (by default set to the duration of the first cut).
58     :return a list of packed cuts.
59     """
60     if len(cuts) <= 1:
61         # Nothing to do.
62         return CutSet.from_cuts(cuts)
63     cuts = sorted(cuts, key=lambda c: c.duration, reverse=True)
64     max_duration = cuts[0].duration if max_duration is None else max_duration
65     current_idx = 1
66     while True:
67         can_fit = False
68         shortest = cuts[-1]
69         for idx in range(current_idx, len(cuts) - 1):
70             cut = cuts[current_idx]
71             can_fit = cut.duration + gap + shortest.duration <= max_duration
72             if can_fit:
73                 cuts[current_idx] = cut.pad(cut.duration + gap).append(shortest)
74                 cuts = cuts[:-1]
75                 break
76             current_idx += 1
77         if not can_fit:
78             break
79     return CutSet.from_cuts(cuts)
80
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/lhotse/dataset/cut_transforms/concatenate.py b/lhotse/dataset/cut_transforms/concatenate.py
--- a/lhotse/dataset/cut_transforms/concatenate.py
+++ b/lhotse/dataset/cut_transforms/concatenate.py
@@ -62,7 +62,7 @@
         return CutSet.from_cuts(cuts)
     cuts = sorted(cuts, key=lambda c: c.duration, reverse=True)
     max_duration = cuts[0].duration if max_duration is None else max_duration
-    current_idx = 1
+    current_idx = 0
     while True:
         can_fit = False
         shortest = cuts[-1]

{"golden_diff": "diff --git a/lhotse/dataset/cut_transforms/concatenate.py b/lhotse/dataset/cut_transforms/concatenate.py\n--- a/lhotse/dataset/cut_transforms/concatenate.py\n+++ b/lhotse/dataset/cut_transforms/concatenate.py\n@@ -62,7 +62,7 @@\n         return CutSet.from_cuts(cuts)\n     cuts = sorted(cuts, key=lambda c: c.duration, reverse=True)\n     max_duration = cuts[0].duration if max_duration is None else max_duration\n-    current_idx = 1\n+    current_idx = 0\n     while True:\n         can_fit = False\n         shortest = cuts[-1]\n", "issue": "Cut concatenate doesn't consider the first sample in each batch\nFound in #234 \n", "before_files": [{"content": "from typing import Optional, Sequence\n\nfrom lhotse import CutSet\nfrom lhotse.cut import AnyCut\nfrom lhotse.utils import Seconds\n\n\nclass CutConcatenate:\n    \"\"\"\n    A transform on batch of cuts (``CutSet``) that concatenates the cuts to minimize the total amount of padding;\n    e.g. instead of creating a batch with 40 examples, we will merge some of the examples together\n    adding some silence between them to avoid a large number of padding frames that waste the computation.\n    \"\"\"\n\n    def __init__(\n            self,\n            gap: Seconds = 1.0,\n            duration_factor: float = 1.0\n    ) -> None:\n        \"\"\"\n        CutConcatenate's constructor.\n\n        :param gap: The duration of silence in seconds that is inserted between the cuts;\n            it's goal is to let the model \"know\" that there are separate utterances in a single example.\n        :param duration_factor: Determines the maximum duration of the concatenated cuts;\n            by default it's 1, setting the limit at the duration of the longest cut in the batch.\n        \"\"\"\n        self.gap = gap\n        self.duration_factor = duration_factor\n\n    def __call__(self, cuts: CutSet) -> CutSet:\n        cuts = cuts.sort_by_duration(ascending=False)\n        return concat_cuts(\n            cuts,\n            gap=self.gap,\n            max_duration=cuts[0].duration * self.duration_factor\n        )\n\n\ndef concat_cuts(\n        cuts: Sequence[AnyCut],\n        gap: Seconds = 1.0,\n        max_duration: Optional[Seconds] = None\n) -> CutSet:\n    \"\"\"\n    We're going to concatenate the cuts to minimize the amount of total padding frames used.\n    This means that some samples in the batch will be merged together into one sample,\n    separated by an interval of silence.\n    This is actually solving a knapsack problem.\n    In this initial implementation we're using a greedy approach:\n    going from the back (i.e. the shortest cuts) we'll try to concat them to the longest cut\n    that still has some \"space\" at the end.\n\n    :param cuts: a list of cuts to pack.\n    :param gap: the duration of silence inserted between concatenated cuts.\n    :param max_duration: the maximum duration for the concatenated cuts\n        (by default set to the duration of the first cut).\n    :return a list of packed cuts.\n    \"\"\"\n    if len(cuts) <= 1:\n        # Nothing to do.\n        return CutSet.from_cuts(cuts)\n    cuts = sorted(cuts, key=lambda c: c.duration, reverse=True)\n    max_duration = cuts[0].duration if max_duration is None else max_duration\n    current_idx = 1\n    while True:\n        can_fit = False\n        shortest = cuts[-1]\n        for idx in range(current_idx, len(cuts) - 1):\n            cut = cuts[current_idx]\n            can_fit = cut.duration + gap + shortest.duration <= max_duration\n            if can_fit:\n                cuts[current_idx] = cut.pad(cut.duration + gap).append(shortest)\n                cuts = cuts[:-1]\n                break\n            current_idx += 1\n        if not can_fit:\n            break\n    return CutSet.from_cuts(cuts)\n", "path": "lhotse/dataset/cut_transforms/concatenate.py"}], "after_files": [{"content": "from typing import Optional, Sequence\n\nfrom lhotse import CutSet\nfrom lhotse.cut import AnyCut\nfrom lhotse.utils import Seconds\n\n\nclass CutConcatenate:\n    \"\"\"\n    A transform on batch of cuts (``CutSet``) that concatenates the cuts to minimize the total amount of padding;\n    e.g. instead of creating a batch with 40 examples, we will merge some of the examples together\n    adding some silence between them to avoid a large number of padding frames that waste the computation.\n    \"\"\"\n\n    def __init__(\n            self,\n            gap: Seconds = 1.0,\n            duration_factor: float = 1.0\n    ) -> None:\n        \"\"\"\n        CutConcatenate's constructor.\n\n        :param gap: The duration of silence in seconds that is inserted between the cuts;\n            it's goal is to let the model \"know\" that there are separate utterances in a single example.\n        :param duration_factor: Determines the maximum duration of the concatenated cuts;\n            by default it's 1, setting the limit at the duration of the longest cut in the batch.\n        \"\"\"\n        self.gap = gap\n        self.duration_factor = duration_factor\n\n    def __call__(self, cuts: CutSet) -> CutSet:\n        cuts = cuts.sort_by_duration(ascending=False)\n        return concat_cuts(\n            cuts,\n            gap=self.gap,\n            max_duration=cuts[0].duration * self.duration_factor\n        )\n\n\ndef concat_cuts(\n        cuts: Sequence[AnyCut],\n        gap: Seconds = 1.0,\n        max_duration: Optional[Seconds] = None\n) -> CutSet:\n    \"\"\"\n    We're going to concatenate the cuts to minimize the amount of total padding frames used.\n    This means that some samples in the batch will be merged together into one sample,\n    separated by an interval of silence.\n    This is actually solving a knapsack problem.\n    In this initial implementation we're using a greedy approach:\n    going from the back (i.e. the shortest cuts) we'll try to concat them to the longest cut\n    that still has some \"space\" at the end.\n\n    :param cuts: a list of cuts to pack.\n    :param gap: the duration of silence inserted between concatenated cuts.\n    :param max_duration: the maximum duration for the concatenated cuts\n        (by default set to the duration of the first cut).\n    :return a list of packed cuts.\n    \"\"\"\n    if len(cuts) <= 1:\n        # Nothing to do.\n        return CutSet.from_cuts(cuts)\n    cuts = sorted(cuts, key=lambda c: c.duration, reverse=True)\n    max_duration = cuts[0].duration if max_duration is None else max_duration\n    current_idx = 0\n    while True:\n        can_fit = False\n        shortest = cuts[-1]\n        for idx in range(current_idx, len(cuts) - 1):\n            cut = cuts[current_idx]\n            can_fit = cut.duration + gap + shortest.duration <= max_duration\n            if can_fit:\n                cuts[current_idx] = cut.pad(cut.duration + gap).append(shortest)\n                cuts = cuts[:-1]\n                break\n            current_idx += 1\n        if not can_fit:\n            break\n    return CutSet.from_cuts(cuts)\n", "path": "lhotse/dataset/cut_transforms/concatenate.py"}]}